Compact Modeling Framework v3.0 for high-resolution global ocean–ice–atmosphere models

Kalmykov, Vladimir V.; Ibrayev, R. A.; Kaurkin, Maxim; Ushakov, K. V.

doi:10.5194/gmd-11-3983-2018

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…The vertical discretization includes 51 horizons with a step ranging from 2 m near the surface to 100 m at depths greater than 1 km. The model is implemented for massively parallel computations under control of the Compact Modeling Framework [37].…”

Section: Inmio Eddy-resolving Modelmentioning

confidence: 99%

Undercurrents in the Northeastern Black Sea Detected on the Basis of Multi-Model Experiments and Observations

Демышев

Дымова

Markova

et al. 2021

JMSE

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Numerical simulation results of the Black Sea circulation obtained by four ocean dynamics models are compared to each other and to in situ data in order to determine the features of the Black Sea deep-water circulation such as deep-water undercurrents. The year 2011 is chosen as the test period due to the availability of deep-sea observations, including ARGO profiles and ADCP current velocities. Validation of the simulation results is based on comparison with the temperature and salinity measured by the ARGO floats. Anticyclonic currents (undercurrents) under the cyclonic Rim Current are detected by the results of all numerical models near the North Caucasian coast. The main characteristics of undercurrents are consistent with in situ data on current velocity up to a depth of 1000 m obtained by the Aqualog probe at the IO RAS test site near Gelendzhik in June 2011. The analysis of the spatio-temporal variability of the modeled salinity and velocity fields reveals that the most probable origin of the undercurrents is the horizontal density gradient of seawater in the region.

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Section: Inmio Eddy-resolving Modelmentioning

confidence: 99%

Undercurrents in the Northeastern Black Sea Detected on the Basis of Multi-Model Experiments and Observations

Демышев

Дымова

Markova

et al. 2021

JMSE

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“…In this work, we use the global ocean -sea ice coupled model, which consists of the INMIO ocean general circulation model (Ibrayev et al, 2012) and the CICE ice dynamics and thermodynamics model (Hunke et al, 2015) operating on massively parallel computers under control of the Compact Modeling Framework (CMF) (Kalmykov et al, 2018).…”

Section: Coupled Ocean-ice Model Setupmentioning

confidence: 99%

“…The model forecast x b is supplied to the service regularly (e.g., once per model day) from all model components via the cluster interconnect without accessing the file system. (Kalmykov et al, 2018).…”

Section: Program Implementation Of Enoimentioning

confidence: 99%

Assimilation of ice compactness data in a strong coupling regime in the ocean – sea ice coupled model

Kaurkin

Kalnitski

Ushakov

et al. 2021

Preprint

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Abstract. The Arctic Ocean plays an important role in the global climate system, where sea ice regulates the exchange of heat, moisture and momentum between the atmosphere and the ocean. A comprehensive analysis and forecast of the Arctic ocean system requires a detailed numerical ocean and sea ice coupled model supplemented by assimilation of observational data at appropriate time scales. A new operative ocean – ice state forecast system was developed and implemented. It consists of the INMIO4.1 ocean general circulation model and the CICE5.1 sea ice dynamics and thermodynamics model with common spatial resolution of 0.25°. For the exchange of boundary conditions and service actions (data storage, time synchronization, etc.), the coupled model uses the Compact Modeling Framework (CMF3.0). Data assimilation is implemented in the form of the Data Assimilation Service (DAS) based on the Ensemble Optimal Interpolation (EnOI) method. This technique allows to simultaneously correct the ocean (temperature, salinity, surface level) and ice (concentration) model fields in the DAS service, so they are coordinated not only through the exchange of boundary conditions, but already at the stage of data assimilation (i.e. strong coupling data assimilation). Experiments with the INMIO – CICE model show that the developed algorithm provides a significant improvement in the accuracy of forecasting the state of the ice field in the Arctic Ocean.

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“…Наряду с моделью океана будет использоваться компактная вычислительная платформа CMF3.0 (Compact Modeling Framework) для решения задачи совместного моделирования Земной системы и ее компонентов высокого разрешения на компьютерах с распределенной памятью. Платформа CMF3.0 используется для межпроцессорных обменов внутри модельных компонентов, интерполяции данных между различными модельными сетками компонентов, работы с файловой системой (ввод/вывод данных) [12].…”

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Analyzed is the original parallel realization of the ensemble optimal interpolation (EnOI) method for data assimilation in the ocean dynamics model developed in the Institute of Numerical Mathematics and the Institute of Oceanology (INMIO model) with a resolution 0.1° for the North Atlantic region. Data and methods. Based on the known ("true") model state of the ocean, the temperature profiles (about 70 per day, up to 1500 m depth) were chosen and used as synthetic observational data. After the initial condition was perturbed, the numerical experiments were carried out to estimate speed and accuracy of approaching the entire model solution to the "true" state of the ocean as the temperature profiles were assimilated. Results. Both qualitative analysis of the results and the graphs of the root-mean-square and mean errors of the model solution are given. To study the method sensitivity to the amount of the observational data, the experiments with the decreasing number of data (1/2, 1/4, 1/8 and 1/16 of all the temperature profiles) were performed. They showed that assimilation even of the isolated data could significantly increase quality of the model forecast. Discussion and conclusion. The experiments prove that application of the ensemble optimal interpolation method, even in case very few data are assimilated in the model, can significantly improve quality both of the model forecast and the entire model solution for those regions where the observational data are very scarce or absent at all. Thus due to assimilation of the data covering only 3-4 days, the root-mean-square error for the sea surface temperature model field decreases by 1.5ºC, and the average deviation becomes equal almost to zero over the entire computational domain.

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Compact Modeling Framework v3.0 for high-resolution global ocean–ice–atmosphere models

Cited by 17 publications

References 27 publications

Undercurrents in the Northeastern Black Sea Detected on the Basis of Multi-Model Experiments and Observations

Undercurrents in the Northeastern Black Sea Detected on the Basis of Multi-Model Experiments and Observations

Assimilation of ice compactness data in a strong coupling regime in the ocean – sea ice coupled model

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