Skip to main content

Advertisement

Log in

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

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • 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 4:1147–1167

    Article  Google Scholar 

  • 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)

  • 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

    Article  Google Scholar 

  • 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 184

  • Asselin R (1972) Frequency filter for time integrations. Mon Wea Rev 100:487–490

    Article  Google Scholar 

  • Betts AK (1986) A new convective adjustment scheme. Part 1. Observational and theoretical basis. Quart J Roy Met Soc 112:677–691

    Google Scholar 

  • Butchart N et al (2011) Multimodel climate and variability of the stratosphere. J Geophys Res 116:D05102. doi:10.1029/2010JD014995

    Article  Google Scholar 

  • Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  • 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, Cambridge

  • Galin VYa (1998) Parametrization of radiative processes in the DNM atmospheric model. Izv Atmos Ocean Phy 34:339–347

    Google Scholar 

  • Galin VYa, Volodin EM, Smyshlyaev SP (2003) Atmospheric general circulation model with ozone dynamics. Rus Meteorol Hydrol N5:7–15

    Google Scholar 

  • 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 59:387–400

    Article  Google Scholar 

  • 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 28:911–930

    Article  Google Scholar 

  • 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 21:5145–5153

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 6:2001

    Google Scholar 

  • Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. Meteor. Monogr. 10. N32, Amer Meteor Soc p 84

  • 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 45:37–54

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Loeb NG et al (2009) Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J Climate 22:748–766

    Article  Google Scholar 

  • 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 25:5327–5342

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mueller B, Seneviratne SI (2014) Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys Res Lett  41:128–134. doi:10.1002/2013GL058055

    Article  Google Scholar 

  • 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 112:1001–1031

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Stephens BB et al (2007) Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316:1732–1735

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Met Soc 93:485–498

    Article  Google Scholar 

  • 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):189–208

    Google Scholar 

  • Tiedtke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061

    Article  Google Scholar 

  • 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 9:14,437–14,473

    Article  Google Scholar 

  • Trenberth KE, Caron JM (2001) Estimates of meridional atmosphere and ocean heat transports. J Clim 14:3433–3443

    Article  Google Scholar 

  • 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 52:1–15

    Article  Google Scholar 

  • Volodin EM (2007) Atmosphere—ocean general circulation model with carbon cycle. Izv Atmos Ocean Phy 43:266–280

    Article  Google Scholar 

  • Volodin EM (2008) Methane cycle in the INM RAS climate model. Izv Atmos Ocean Phy 44:153–159

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Volodin EM, Kostrykin SV (2016) The Aerosol module in the INM RAS climate model. Rus Meteorol Hydrol 41(8):519–528

    Article  Google Scholar 

  • 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 34:405–416

    Google Scholar 

  • 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 46:414–431

    Article  Google Scholar 

  • 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 46:347–366

    Article  Google Scholar 

  • Welp LR et al (2011) Interannual variability in the oxygen isotopes of atmospheric CO2 driven by El Ni ~ no. Nature 477:579–582

    Article  Google Scholar 

  • 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) 581–609

    Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. M. Volodin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Volodin, E.M., Mortikov, E.V., Kostrykin, S.V. et al. Simulation of the present-day climate with the climate model INMCM5. Clim Dyn 49, 3715–3734 (2017). https://doi.org/10.1007/s00382-017-3539-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-017-3539-7

Keywords

Navigation