Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 6, pp. 111-124

Applying Satellite Data for Validation of the Hydrodynamic Model for the Arctic Ocean

A.V. Koldunov 1 , N.V. Koldunov 2 , D.L. Volkov 3 , T.V. Belonenko 1 
1 Saint-Petersburg State University, Saint-Petersburg, Russia
2 Climate Service Center 2.0, HZG, Hamburg, Germany
3 Cooperative Institute for Marine and Atmospheric Studies, University of Miami, and NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami FL, USA
The aim of this work is to test the performance of the MITgcm hydrodynamic model setup for the Arctic Ocean which is installed and runs in Resource Center “Computer Center of the St. Petersburg State University”. The setup is created in the framework of the ECCO2 project that aims to simulate global high-resolution fields of oceanographic characteristics with linear approximation to oceanographic observations. Satellite data are used to validate several oceanographic features simulated by the model: the sea ice extent and concentration, sea level and sea surface temperature. The model is able to successfully reproduce spatial and temporal variability of sea ice characteristics and give adequate estimates of seasonal variability and trend of the sea ice extent in the Arctic Ocean. Simulation of sea surface height anomalies associated with mass change is satisfactory for the Arctic Ocean regions located away from the North Pole, particularly for the Barents Sea. Simulation of sea surface temperature demonstrates good results for both interannual variability and trend. Comparison of the satellite- and model-derived data proves that the model reproduces the above mentioned oceanographic features reasonably well. Therefore, this model may be used in further studies different scientific and practical problems of the Arctic Ocean.
Keywords: Arctic Ocean, MITgcm hydrodynamic model, ECCO2, validation, satellite data, sea ice concentration, sea ice extent, sea level, sea surface temperature
Full text

References:

  1. Ivanov V.V., Alexeev V.A., Alexeeva T.A., Koldunov N.V., Repina I.A., Smirnov A. V., Arkticheskij ledjanoj pokrov stanovitsja sezonnym? (Does Arctic Ocean Ice Cover Become Seasonal?), Issledovanie Zemli iz kosmosa, 2013, No. 4, pp. 50–65.
  2. Shalina E.V., Johannessen O.M., Bobylev L.P., Izmenenie arkticheskogo ledjanogo pokrova po dannym sputnikovogo passivnogo mikrovolnovogo zondirovanija s 1978 po 2007 gody (Changes in Arctic ice cover from satellite passive microwave sensing in 1978 and 2007), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa , 2008, Vol. 5, No. 2, pp. 228–233.
  3. Shalina E.V., Sokrashhenie ledjanogo pokrova Arktiki po dannym sputnikovogo passivnogo mikrovolnovogo zondirovanija (Arctic sea ice retreat from satellite passive microwave observations), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol.10, No. 1, pp. 328–336.
  4. Antonov J.I., Seidov D., Boyer T.P., Locarnini R.A., Mishonov A.V., Garcia H.E., Baranova O.K., M. Zweng M., Johnson D. R., World Ocean Atlas 2009, Volume 2: Salinity. S. Levitus, Ed. NOAA Atlas NESDIS 69, U.S. Government Printing Office, 2010, Washington, D.C., 184 pp.
  5. Cavalieri, D.J., Parkinson C.L., Gloersen P., Zwally H., Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, 1996, updated yearly. http://dx.doi.org/10.5067/8GQ8LZQVL0VL.
  6. Chambers D.P., Willis J.K., A Global Evaluation of Ocean Bottom Pressure from GRACE, OMCT, and Steric-Corrected Altimetry, J. Oceanic and Atmos. Technology, 2010. Vol. 27, pp. 1395–1402. DOI: 10.1175/2010JTECHO738.1.
  7. Chambers D.P., Bonin J.A., Evaluation of Release 05 time-variable gravity coefficients over the ocean, Ocean Science, 2012. Vol. 8, pp. 859–868, www.ocean-sci.net/8/859/2012.
  8. Daru V., Tenaud C., High order one-step monotonicitypreserving schemes for unsteady compressible flow calculations, J. Comput. Phys., 2004, Vol. 193, No. 2, pp. 563–594, doi:10.1016/j.jcp.2003.08.023.
  9. Fox-Kemper B., Menemenlis D., Can large eddy simulation techniques improve mesoscale rich ocean models? In: Ocean Modeling in an Eddying Regime, Geophys. Monogr. Ser. , edited by M. Hecht and H. Hasumi. AGU, Washington, D.C., 2008, Vol. 177, pp. 319–338.
  10. Jackett D.R., McDougall T.J., Minimal adjustment of hydrographic profiles to achieve static stability, J. Atmos. Oceanic Technol., 1995, No. 12, pp. 381–389.
  11. Large W.G., McWilliams J.C., Doney S., Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 1994, Vol. 32, No. 4, pp.. 363–403.
  12. Large W., Yeager S., Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, Tech. Note NCAR/TN-460+STR, Natl. Cent. for Atmos. Res. , Boulder, Colo, 2004,. 111 p., doi:10.5065/D6KK98Q6.
  13. Leith C.E., Stochastic models of chaotic systems, Phys. D., 1996, No. 98. pp. 481–491.
  14. Locarnini R.A., Mishonov A.V., Antonov J.I., Boyer T.P., Garcia H.E., Baranova O. K, Zweng M.M., Johnson D.R., World Ocean Atlas 2009, Volume 1: Temperature. S. Levitus, Ed. NOAA Atlas NESDIS 68, U.S. Government Printing Office, Washington, D.C., 2010, 184 pp.
  15. Losch M., Menemenlis D., Heimbach P., Campin J.-M., Hill C. On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations, Ocean Modell., 2010, No. 33, pp. 129–144.
  16. Marshall, J., Adcroft A.,. Hill C, Perelman L., Heisey C., A finite-volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers, J. Geophys. Res., 1997, 102(C3), pp. 5753–5766.
  17. Menemenlis D., Campin J., Heimbach P., Hill C., Lee T., Nguyen A., Schodlock M., Zhang H., ECCO2: High resolution global ocean and sea ice data synthesis, Mercator Ocean Q. Newsl., 2008, No. 31, pp. 13–21.
  18. Nguyen, A. T., Menemenlis D., Kwok R., Arctic ice‐ocean simulation with optimized model parameters: Approach and assessment,. J. Geophys. Res., 2011, No. 116, C04025. doi:10.1029/2010JC006573.
  19. Reynolds R.W., What's New in Version 2. OISST Webpage, http://www.ncdc.noaa.gov/sst/papers/oisst_daily_v02r00_version2-features.pdf. 2009, 10 p.
  20. Reynolds R.W., Smith T.M., Liu C., Chelton D.B., Casey K.S., Schlax M.G., Daily high-resolution-blended analyses for sea surface temperature, Journal of Climate, 2007, No. 20, pp. 5473–5496, doi:10.1175/2007JCLI1824.1.
  21. Volkov D.L., Landerer F.W., Non-seasonal variability of the Arctic Ocean mass observed by the GRACE satellites, J. Geophys. Res., 2013, V.118, pp. 6451–6460, doi:10.1002/2013JC009341.
  22. Wahr J., Swenson S., Velicogna I., Accuracy of GRACE mass estimates, Geophys. Res. Lett., 2006, No. 33, L06401, pp. 1-5, doi: 10.1029/2005GL025305.
  23. Zhang J., Hibler W.D., On an efficient numerical method for modeling sea ice dynamics, J. Geophys. Res., 1997, 102(C4), pp. 8691–8702.