Climate Dynamics

, Volume 49, Issue 11–12, pp 3715–3734 | Cite as

Simulation of the present-day climate with the climate model INMCM5

  • E. M. VolodinEmail author
  • E. V. Mortikov
  • S. V. Kostrykin
  • V. Ya. Galin
  • V. N. Lykossov
  • A. S. Gritsun
  • N. A. Diansky
  • A. V. Gusev
  • N. G. Iakovlev


In this paper we present the fifth generation of the INMCM climate model that is being developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences (INMCM5). The most important changes with respect to the previous version (INMCM4) were made in the atmospheric component of the model. Its vertical resolution was increased to resolve the upper stratosphere and the lower mesosphere. A more sophisticated parameterization of condensation and cloudiness formation was introduced as well. An aerosol module was incorporated into the model. The upgraded oceanic component has a modified dynamical core optimized for better implementation on parallel computers and has two times higher resolution in both horizontal directions. Analysis of the present-day climatology of the INMCM5 (based on the data of historical run for 1979–2005) shows moderate improvements in reproduction of basic circulation characteristics with respect to the previous version. Biases in the near-surface temperature and precipitation are slightly reduced compared with INMCM4 as well as biases in oceanic temperature, salinity and sea surface height. The most notable improvement over INMCM4 is the capability of the new model to reproduce the equatorial stratospheric quasi-biannual oscillation and statistics of sudden stratospheric warmings.


Climate Model Atmosphere Ocean Parameterization Simulation Temperature Precipitation Bias 



The study was performed at the Institute of Numerical Mathematics of the Russian Academy of Sciences and supported by the Russian Science Foundation, grant 14-17-00126 (model development) and Russian Foundation for Basic Research, grant 16-55-76004 ERA.NET RUS (numerical experiments). Climate model runs were produced at the supercomputer MVS10P of the Joint Supercomputer Center of the Russian Academy of Sciences.


  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P (2003) The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J Hydrometeor 4:1147–1167CrossRefGoogle Scholar
  2. Alekseev VA, Volodin EM, Galin VYa, Dymnikov VP, Lykossov VN (1998) Simulation of present day climate with atmospheric model of INM RAS. INM Reprint, p 198, (available by request)Google Scholar
  3. Anav A, Friedlingstein P, Kidston M, Bopp L, Ciais P, Cox P, Jones C, Jung M, Myneni R, Zhu Z (2013) Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models. J Climate 26:6801–6843. doi: 10.1175/JCLI-D-12-00417.1 CrossRefGoogle Scholar
  4. Antonov JI et al (2010) World ocean atlas 2009, Vol. 2: Salinity. [S. Levitus (eds.)]. NOAA Atlas NESDIS 69, U.S. Gov. Printing Office, Washington, D.C., pp 184Google Scholar
  5. Asselin R (1972) Frequency filter for time integrations. Mon Wea Rev 100:487–490CrossRefGoogle Scholar
  6. Betts AK (1986) A new convective adjustment scheme. Part 1. Observational and theoretical basis. Quart J Roy Met Soc 112:677–691Google Scholar
  7. Butchart N et al (2011) Multimodel climate and variability of the stratosphere. J Geophys Res 116:D05102. doi: 10.1029/2010JD014995 CrossRefGoogle Scholar
  8. Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597CrossRefGoogle Scholar
  9. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013) Evaluation of climate models. In: Climate change 2013: the physical science basis. contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds)]. Cambridge University Press, CambridgeGoogle Scholar
  10. Galin VYa (1998) Parametrization of radiative processes in the DNM atmospheric model. Izv Atmos Ocean Phy 34:339–347Google Scholar
  11. Galin VYa, Volodin EM, Smyshlyaev SP (2003) Atmospheric general circulation model with ozone dynamics. Rus Meteorol Hydrol N5:7–15Google Scholar
  12. Hines CO (1997) Doppler spread parameterization of gravity wave momentum deposition in the middle atmosphere 2. Broad and quasimonochromatic spectra, and implementation. J Atm Sol Terr Phys 59:387–400CrossRefGoogle Scholar
  13. Huang B, Banzon VF, Freeman E, Lawrimore J, Liu W, Peterson TC, Smith TM, Thorne PW, Woodruff SD, Zhang H-M (2015) Extended reconstructed sea surface temperature version 4 (ERSST.v4): part I. upgrades and intercomparisons. J Climate 28:911–930CrossRefGoogle Scholar
  14. Hurrell JW, Hack JJ, Shea D, Caron JM, Rosinski J (2008) A new sea surface temperature and sea ice boundary dataset for the community atmosphere model. J Climate 21:5145–5153CrossRefGoogle Scholar
  15. Iakovlev NG (2009) Reproduction of the large scale state of water and sea ice in the Arctic Ocean in 1948–2002. Part 1. Numerical model. Izv Atmos Ocean Phy 45:357–371. doi: 10.1134/S0001433809030098 CrossRefGoogle Scholar
  16. Iakovlev NG, Volodin EM, Gritsun AS (2016) Simulation of the spatiotemporal variability of the World Ocean sea surface height by the INM climate models. Izv Atmos Ocean Phy 52(4):376–385. doi: 10.1134/S0001433816040125 CrossRefGoogle Scholar
  17. Jung M, Reichstein M, Bondeau A (2009) Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 6:2001Google Scholar
  18. Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. Meteor. Monogr. 10. N32, Amer Meteor Soc p 84Google Scholar
  19. Kulyamin DV, Volodin EM, Dymnikov VP (2009) Simulation of the quasi-biannual oscillation in the zonal wind in the equatorial stratosphere: Part II. Atmospheric general circulation models. Izv Atmos Ocean Phy 45:37–54CrossRefGoogle Scholar
  20. Landerer FW, Gleckler PJ, Lee T (2014) Evaluation of CMIP5 dynamic sea surface height multi-model simulations against satellite observations. Clim Dyn 43:1271–1283. doi: 10.1007/s00382-013-1939-x CrossRefGoogle Scholar
  21. Loeb NG et al (2009) Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J Climate 22:748–766CrossRefGoogle Scholar
  22. Mao J, Thornton P, Shi X, Zhao M, Post W (2012) Remote sensing evaluation of CLM4 GPP for the period 2000 to 2009. J Climate 25:5327–5342CrossRefGoogle Scholar
  23. Mareev EA, Volodin EM (2014) Variations of the global electric circuit and the ionospheric potential in a general circulation model. Geophys Res Lett 41:9009–9016. doi: 10.1002/2014GL062352 CrossRefGoogle Scholar
  24. Mueller B, Seneviratne SI (2014) Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys Res Lett  41:128–134. doi: 10.1002/2013GL058055 CrossRefGoogle Scholar
  25. Palmer TN, Shutts GJ, Swinbank R (1986) Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parameterization. Quart J Roy Met Soc 112:1001–1031CrossRefGoogle Scholar
  26. Rio M-H, Hernandez F (2004) A mean dynamical topography computed over the world ocean from altimetry, in-situ measurements and a geoid model. J Geophys Res 109:C12032. doi: 10.1029/2003JC002226 CrossRefGoogle Scholar
  27. Stephens BB et al (2007) Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316:1732–1735CrossRefGoogle Scholar
  28. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Met Soc 93:485–498CrossRefGoogle Scholar
  29. Terekhov KM, Volodin EM, Gusev AV (2011) Methods and efficiency estimation of parallel implementation of the σ-model of general ocean circulation. Russ J Num Anal Math Model 26(2):189–208Google Scholar
  30. Tiedtke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061CrossRefGoogle Scholar
  31. Todd-Brown KEO, Randerson JT, Post WM, Hoffman FM, Tarnocai C, Schuur EA, Allison SD (2012) Causes of variation in soil carbon predictions from CMIP5 Earth system models and comparison with observations. Biogeosci Discuss 9:14,437–14,473CrossRefGoogle Scholar
  32. Trenberth KE, Caron JM (2001) Estimates of meridional atmosphere and ocean heat transports. J Clim 14:3433–3443CrossRefGoogle Scholar
  33. Vargin PN, Volodin EM (2016) Analysis of the reproduction of dynamic processes in the stratosphere using the climate model of the Institute of Numerical Mathematics, Russian Academy of Sciences. Izv Atmos Ocean Phy 52:1–15CrossRefGoogle Scholar
  34. Volodin EM (2007) Atmosphere—ocean general circulation model with carbon cycle. Izv Atmos Ocean Phy 43:266–280CrossRefGoogle Scholar
  35. Volodin EM (2008) Methane cycle in the INM RAS climate model. Izv Atmos Ocean Phy 44:153–159CrossRefGoogle Scholar
  36. Volodin EM (2013) The mechanism of multidecadal variability in the Arctic and North Atlantic in climate model INMCM4. Environ Res Lett 8:035038. doi: 10.1088/1748-9326/8/3/035038 CrossRefGoogle Scholar
  37. Volodin EM, Kostrykin SV (2016) The Aerosol module in the INM RAS climate model. Rus Meteorol Hydrol 41(8):519–528CrossRefGoogle Scholar
  38. Volodin EM, Lykosov VN (1998) Parametrization of neat and moisture transfer in the soil-vegetation system for use in atmospheric general circulation models: 1. Formulation and simulations based on local observational data. Izv Atmos Ocean Phy 34:405–416Google Scholar
  39. Volodin EM, Dianskii NA, Gusev AV (2010) Simulating present day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv Atmos Ocean Phy 46:414–431CrossRefGoogle Scholar
  40. Volodin EM, Diansky NA, Gusev AV (2013) Simulation and prediction of climate changes in the 19th to 21st centuries with the Institute of Numerical Mathematics, Russian Academy of Sciences, model of Earth climate system. Izv Atmos Ocean Phy 46:347–366CrossRefGoogle Scholar
  41. Welp LR et al (2011) Interannual variability in the oxygen isotopes of atmospheric CO2 driven by El Ni ~ no. Nature 477:579–582CrossRefGoogle Scholar
  42. Zalesny VB, Marchuk GI, Agoshkov VI, Bagno AV, Gusev AV, Diansky NA, Moshonkin SN, Tamsalu R, Volodin EM (2010) Numerical simulation of large-scale ocean circulation based on the multicomponent splitting method. Russ J Num Anal Math Model 25(6) 581–609Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Institute of Numerical MathematicMoscowRussia
  2. 2.Moscow State UniversityMoscowRussia

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