Home » Archive of journals » Volume 11, No. 2, 2021 » Operational forecasting system for Arctic Ocean using the Russian marine circulation model INMOM-Arctic
OPERATIONAL FORECASTING SYSTEM FOR ARCTIC OCEAN USING THE RUSSIAN MARINE CIRCULATION MODEL INMOM-ARCTICJOURNAL: Volume 11, No. 2, 2021, p. 205-218
HEADING: Research activities in the Arctic
AUTHORS: Fomin, V.V., Panasenkova, I.I., Gusev, A.V., Chaplygin, A.V., Diansky, N.A.
ORGANIZATIONS: Lomonosov Moscow State University, N. N. Zubov’s State Oceanographic Institute, Marchuk Institute of Numerical Mathematics of RAS
The article was received on: 03.02.2021
Keywords: Arctic Ocean, sea ice, numerical modeling, ocean model, marine circulation, data assimilation
Bibliographic description: Fomin, V.V., Panasenkova, I.I., Gusev, A.V., Chaplygin, A.V., Diansky, N.A. Operational forecasting system for Arctic Ocean using the Russian marine circulation model INMOM-Arctic. Arktika: ekologiya i ekonomika. [Arctic: Ecology and Economy], 2021, vol. 11, no. 2, pp. 205-218. DOI: 10.25283/2223-4594-2021-2-205-218. (In Russian).
A regional σ-model INMOM-Arctic has been prepared on the basis of the Russian ocean general circulation model INMOM (Institute of Numerical Mathematics Ocean Model) to reproduce the current state and short-term forecast of the Arctic Ocean (AO) hydrothermodynamics. The model is implemented in a rotated spherical coordinate system with the poles located at 60°E and 120° W on the geographic equator, which makes it possible to use a quasi-uniform resolution of ~ 3,7 km in the Arctic Basin. Data on temperature, salinity, horizontal velocity components and sea level taken from the CMEMS ocean products are used at the AO open boundaries. To take into account the tidal effect in the INMOM-Arctic model at open boundaries, the time series of the tidal sea level is set based on the data of the TPXO 9 atlas (TOPEX/Poseidon Global Tidal Model) with a spatial resolution of 1/30°. To calculate the atmospheric impact, the researches use the atmospheric circulation data from the Era 5 global reanalysis with a spatial resolution of 0,25×0,25° and with a temporal resolution of 1 hour.
While estimating the accuracy in the temperature and salinity fields reconstructed by the INMOM-Arctic model on a seasonal scale the authors used the data from ARGO profiler buoys in retrospective simulations of the AO circulation for the period from March 1 to August 31, 2020, without assimilating observational data. They compared the estimates with similar ones from the CMEMS for the Arctic Basin for mean deviation and RMSD within close layers for temperature and salinity fields, respectively. In the temperature field, the INMOM-Arctic results have comparable and better indicators in terms of the mean deviation throughout the entire AO, and somewhat worse, in terms of the RMSD temperature in the upper 300 m layer, which, according to the INMOM-Arctic results, is slightly higher than according to the CMEMS. In deeper layers, on the contrary, in the INMOM-Arctic it is less or comparable.
In the salinity field, the accuracy estimates obtained from the CMEMS data and the INMOM-Arctic model are comparable. In the near-surface layers, the RMSD in the INMOM-Arctic model is slightly higher than the analogous values obtained by CMEMS. However, the INMOM-Arctic model gives slightly better estimates in the mean deviations from the temperature and salinity measurements in depth. The RMSD between the simulated and measured sea level in the INMOM-Arctic model is 6 cm, while in the CMEMS it is 7 cm. In addition, the INMOM-Arctic model reproduces well the thermohaline state of the AO on synoptic scales, which is clear via comparison between simulation results and measured data of temperature and salinity profiles by the ARGO profiler buoys.
The results of comparison between the simulated and observed data indicate that in the current implementation the INMOM-Arctic model using the proposed open boundary conditions makes it possible to reproduce correctly the circulation in the AO.
In the next series of experiments, to improve the accuracy of simulations, the researchers implemented the procedure for assimilation of current satellite data on ice compactness and SST, based on the DART (Data Assimilation Research Testbed) software package (https://dart.ucar.edu/), using the EnKF — Ensemble Kalman Filter, in the INMOM-Arctic model. Thus, they proved that the assimilation of the satellite-measured SST and ice compactness makes it possible to improve the simulation results through the daily assimilation of satellite information.
Finance info: The work was supported by the RSF (grant No. 17-17-01295) and the RFBR (grant No. 18-05-60111), as well as within the framework of the scientific and educational center “Russian Arctic: new materials, technologies and research methods” under the project “Development of the marine hindcast and forecasting system to ensure amplification of the Russian Arctic economic potential” (direction “The Northern Sea Route and connectivity of the Arctic territories”).. Numerical simulations were performed with the hardware of the Interdepartmental Multiprocessing Supercomputer Center of the RAS (http://www.jscc.ru/), the parallel implementation of the INMOM model was carried out within the framework of the RFBR (grant No. 20-31-90109_Postgraduates).
1. Maltrud M. E., McClean J. L. An eddy resolving global 1/10° ocean simulation. Ocean Modeling, 2005, vol. 8 (1—2), ðð. 31—54. DOI: 10.1016/j.ocemod.2003.12.001.
2. Chen C., Gao G., Zhang Y., Beardsley R. C., Lai Z., Qi J., Lin H. Circulation in the arctic ocean: Results from a high-resolution coupled ice-sea nested global-fvcom and arctic-fvcom system. Progress in Oceanography, 2016, vol. 141, ðð. 60—80. DOI: 10.1016/j.pocean.2015.12.002.
3. Sakov P., Counillon F., Bertino L, Lisæter K. A., Oke P. R., Korablev A. TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Science, 2012, vol. 8 (4), ðð. 633—656. DOI: 10.5194/osd-9-1519-2012.
4. Hunke E. C., Lipscomb W. H., Turner A. K., Jeffery N., Elliott S. CICE: The Los Alamos Sea Ice Model Documentation and Software User’s Manual Version 5.1 (Tech. Rep. LA-CC-06–012). Los Alamos, NM, Los Alamos National Laboratory, 2015.
5. Xie J., Bertino L., Counillon F., Lisæter K. A., Sakov P. Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991—2013. Ocean Science, 2017, vol. 13, ðð. 123—144. DOI: 10.5194/os-13-123-2017.
6. Kalnitskii L. Y., Kaurkin M. N., Ushakov K. V., Ibrayev R. A. Seasonal Variability of Water and Sea-Ice Circulation in the Arctic Ocean in a High-Resolution Model. Izvestiya. Atmospheric and Oceanic Physics, 2020, vol. 56 (5), ðð. 522—533. DOI: 10.1134/S0001433820050060.
7. Kulakov M. Y., Makshtas A. P., Shutilin S. V. Coupled ice-ocean model for the Arctic Ocean. Problemy Arktiki i Antarktiki. [Arctic and Antarctic Research], 2012, vol. 2 (92), pp. 6—18. (In Russian).
8. Golubeva E., Platov G., Malakhova V., Kraineva M., Iakshina D. Modelling the long-term and inter-annual variability in the Laptev Sea hydrography and subsea permafrost state. Polarforschung, 2018, vol. 87 (2), ðð. 195—210. DOI: 10.2312/polarforschung.87.2.195.
9. Hvatov A., Nikitin N. O., Kalyuzhnaya A. V., Kosukhin S. S. Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation. Ocean Modelling, 2019, vol. 141, 101427. DOI: 10.1016/j.ocemod.2019.101427.
10. Diansky N. A. Ocean circulation modelling and research of its response to short-term and long-term atmospheric forcing. Moscow, Fizmatlit, 2013, 272 p. (In Russian).
11. Diansky N. A., Panasenkova I. I., Fomin V. V., Gusev A. V., Kabatchenko I. M. Marine Hindcast and Forecasting system for the Russian Arctic seas. Mor. inform.-upravlyayushchie sistemy, 2020, vol. 17 (1), pp. 44—51. Available at: http://oceanplatform.ru/journal/. (In Russian).
12. Volodin E. M., Mortikov E. V., Kostrykin S. V., Galin V. Ya., Lykossov V. N., Gritsun A. S., Diansky N. A., Gusev A. V., Iakovlev N. G. Simulation of the present-day climate with the climate model INMCM5. Climate. Dynamics, 2017. DOI: 10.1007/s00382-017-3539-7.
13. Danabasoglu G., Yeager S. G., Kim W. M. et al. North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Pt. II: Inter-annual to decadal variability. Ocean Modelling, 2016, vol. 97, ðð. 65—90. DOI: 10.1016/j.ocemod.2013.10.005.
14. Blumberg A. F., Mellor G. L. A description of a three-dimensional hydrodynamic model of New York harbor region. J. Hydr. Eng., 1987, vol. 125, ðð. 799—816.
15. Shchepetkin A. F., McWillams J. C. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 2005, vol. 9, ðð. 347—404. DOI:10.1016/j.ocemod.2004.08.002.
16. Moshonkin S., Zalesny V., Gusev A. Simulation of the Arctic — North Atlantic Ocean Circulation with a Two-Equation K-Omega Turbulence Parameterization. J. of Marine Science and Engineering, 2018, vol. 6, no. 3, 95. DOI: 10.3390/jmse6030095.
17. Becker J. J., Sandwell D. T., Smith W. F., Braud J. et al. Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy, 2009, vol. 32 (4), ðð. 355—371. DOI: 10.1080/01490410903297766.
18. Popov S. K., Lobov A. L., Elisov V. V., Batov V. I. Tide in operational model for short-term forecast of sea level and current velocity in the White and Barents Seas. Meteorologiya i gidrologiya, 2013, vol. 6, pp. 68—82. (In Russian).
19. Egbert G. D., Erofeeva S. Y. Efficient inverse modeling of barotropic ocean tides. J. of Atmospheric and Oceanic Technology, 2002, vol. 19.2, ðð. 183—204.
20. Woodgate R. A. Increases in the Pacific inflow to the Arctic from 1990 to 2015, and insights into seasonal trends and driving mechanisms from year-round Bering Strait mooring data. Prog. Oceanogr., 2018, vol. 160, ðð. 124—154. DOI: 10.1016/j.pocean.2017.12.007.
21. Melsom A., Simonsen M., Bertino L., Hackett B., Waagbø G. A., Raj R. Quality Information Document for Arctic Ocean Physical Analysis and Forecast Product ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_A. CMEMS internal report. 2020, iss. 6.1. Available at: https://resources.marine.copernicus.eu/documents/QUID/CMEMS-ARC-QUID-002-001a.pdf.
22. Anderson J. L., Hoar T., Raeder K., Liu H., Collins N., Torn R., Arellano A. The Data Assimilation Research Testbed: A Community Facility. Bull. of the American Meteorological Society, 2009, vol. 90, ðð. 1283—1296. DOI: 10.1175/2009BAMS2618.1.
23. Skamarock W. C., Klemp J. B., Dudhia J. et al. A description of the advanced research WRF Version 3. NCAR Technical Notes, 2008.
24. McCaa J. R., Rothstein M., Eaton B. E., Rosinski J. M., Kluzek E., Vertenstein M. User’s Guide to the NCAR Community Atmosphere Model (CAM 3.0). Technical Report NCAR, Boulder, Colorado, 2004. Available at: http://www.ccsm.ucar.edu/models/atm-cam/.
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