Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975–2017 period at two spatial resolutions, corresponding to ~ 180 and ~ 100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model’s ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Niño-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

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 

  • Bretherton CS, Park S (2009) A new moist turbulence parameterization in the community atmosphere model. J Clim 22:3422–3448

    Article  Google Scholar 

  • Brient F, Roehrig R, Voldoire A (2019) Evaluating marine stratocumulus clouds in the CNRM-CM6-1 model using short-term hindcasts. J Adv Model Earth Syst 11:127–148. https://doi.org/10.1029/2018MS001461

    Article  Google Scholar 

  • Cavalcanti IFA, Raia A (2017) Lifecycle of South American monsoon system simulated by CPTEC/INPE AGCM. Int J Climatol 37(51):878–896. https://doi.org/10.1002/joc.5044

    Article  Google Scholar 

  • Cavalcanti IFA, Marengo JA, Satyamurty P, Trosnikov I, Bonatti J, Nobre CA, D’Almeida C, Sampaio G, Castro CAC, Camargo H, Sanches MB (2002) Global climatological features in a simulation using CPTEC/COLA AGCM. J Clim 15:2965–2988

    Article  Google Scholar 

  • Cavalcanti IFA, Silveira VP, Figueroa SN, Kubota PY, Bonatti JP, Souza DC (2020) Climate variability over South America-regional and large scale features simulated by the Brazilian Atmospheric Model (BAM-v0). Int J Climatol 40(5):2845–2869. https://doi.org/10.1002/joc.6370

    Article  Google Scholar 

  • Chou MD, Suarez, MJ (1999) A solar radiation parameterization (CLIRAD-SW) for atmospheric studies. In: NASA/TM-1999-104606, M. J. Suarez, Ed., Series on global modeling and data assimilation, vol 15, p 40

  • Chou MD, Suarez MJ, Liang XZ, Yan MM, Cote C (2001) A thermal infrared radiation parameterization for atmospheric studies. NASA/TM-2001-104606, vol 19. https://ntrs.nasa.gov/citations/20010072848

  • Coelho CAS (2013) Comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil. Meteorol Appl 20:236–245

    Article  Google Scholar 

  • Coelho CAS, Costa SMS, Freitas SR, Ito ER, Luz G, Santos AF, Nobre CA, Marengo JA, Pezza AB (2012) Climate diagnostics of three major drought events in the Amazon and illustrations of their seasonal precipitation predictions. Meteorol Appl 19:237–255

    Article  Google Scholar 

  • Figueroa SN, Bonatti JP, Kubota PY, Grell GA, Morrison H, Barros SRM, Fernandez JPR, Ramirez E, Siqueira L, Luzia G, Silva J, Silva JR, Pendaharkar J, Capistrano VB, Alvim DS, Enoré DP, Diniz FLR, Satyamurti P, Cavalcanti IFA, Nobre P, Barbosa HMJ, Mendes CL, Panetta J (2016) The Brazilian global atmospheric model (BAM): performance for tropical rainfall forecasting and sensitivity to convective scheme and horizontal resolution. Weather Forecast 31:1547–1572

    Article  Google Scholar 

  • Gates WL, Boyle J, Covey C, Dease C, Doutriaux C, Drach R, Fiorino M, Gleckler P, Hnilo J, Marlais S, Phillips T, Potter G, Santer B, Sperber K, Taylor K, Williams D (1998) An Overview of the results of the atmospheric model intercomparison project (AMIP I). Bull Am Meteor Soc 73:1962–1970

    Article  Google Scholar 

  • Grell G, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett. https://doi.org/10.1029/2002GL015311

    Article  Google Scholar 

  • Guimarães BS, Coelho CAS, Woolnough SJ, Kubota PY, Bastarz CF, Bonatti JP, Figueroa SN, de Souza DC (2020) Configuration and hindcast quality assessment of a Brazilian global sub-seasonal prediction system. QJRMS 146(728):1067–1084. https://doi.org/10.1002/qj.3725

    Article  Google Scholar 

  • Han J, Pan HL (2011) Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Weather Forecast 26:520–533

    Article  Google Scholar 

  • Hersbach H, de Rosnay P, Bell B, Schepers D, Simmons A, Soci C, Abdalla S, Balmaseda MA, Balsamo G, Bechtold P, Berrisford P, Bidlot J, de Boisséson E, Bonavita M, Browne P, Buizza R, Dahlgren P, Dee D, Dragani R, Diamantakis M, Flemming J, Forbes R, Geer A, Haiden T, Hólm E, Haimberger L, Hogan R, Horányi A, Janisková M, Laloyaux P, Lopez P, Muñoz-Sabater J, Peubey C, Radu R, Richardson D, Thépaut J-N, Vitart F, Yang X, Zsótér E, Zuo H (2018) Operational global reanalysis: progress, future directions and synergies with NWP. In: ERA Report Series, vol 27. https://www.ecmwf.int/node/18765

  • Hersbach H, Bell W, Berrisford P, Horányi A, Muñoz-Sabater J, Nicolas J, Radu R, Schepers D, Simmons A, Soci C, Dee D (2019) Global reanalysis: goodbye ERA-Interim, hello ERA5. ECMWF Newsl 159:17–24. https://doi.org/10.21957/vf291hehd7

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMMmulti-satellite precipitation analysis: quasi-global, multiyear, combined-sensor precipitation estimates at fine scale. J Hydrometeorol 8:38–55

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM multi-satellite precipitation analysis (TMPA). In: Hossain F, Gebremichael M (eds) Satellite rainfall applications for surface hydrology. Springer, Berlin, pp 3–22

    Chapter  Google Scholar 

  • Iacono M, Delamere J, Mlawer E, Shephard M, Clough S, Collins W (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103. https://doi.org/10.1029/2008JD009944

    Article  Google Scholar 

  • Karoly DJ (1989) Southern Hemisphere circulation features associated with El Niño-Southern Oscillation events. J Climate 2:1239–1252

    Article  Google Scholar 

  • Klingaman NP, Martin GM, Moise A (2017) ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models. Geosci Model Dev 10:57–83

    Article  Google Scholar 

  • Kodama C, Yamada Y, Noda AT, Kikuchi K, Kajikawa Y, Nasuno T, Tomita T, Yamaura T, Takahashi TG, Hara M, Kawatani Y, Satoh M, Sugi M (2015) A 20-year climatology of a NICAM AMIP-type simulation. J Meteor Soc Jpn 93:393–424. https://doi.org/10.2151/jmsj.2015-024

    Article  Google Scholar 

  • Kubota PY (2012) Variability of storage energy in the soil-canopy system and its impact on the definition of precipitation standard in South America (in Portuguese with abstract in English). In: Ph. D. thesis, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, Brazil

  • Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J (1998) The tropical rainfall measuring mission (TRMM) sensor package. J Atmos Ocean Technol 15:809–817

    Article  Google Scholar 

  • Liebmann B, Smith CA (1996) Description of a complete (interpolated) outgoing longwave radiation dataset. Bull Am Meteor Soc 77:1275–1277

    Google Scholar 

  • Liu X, Wu T, Yang S et al (2017) MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Clim Dyn 48:3283–3307. https://doi.org/10.1007/s00382-016-3264-7

    Article  Google Scholar 

  • Loeb NG et al (2018) Clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J Clim 31(2):895–918

    Article  Google Scholar 

  • Madden RA, Julian PR (1972) Description of global-scale circulation cells in the Tropics with a 40–50 day period. J Atmos Sci 29:1109–1123

    Article  Google Scholar 

  • Marengo JA, Cavalcanti IFA, Satyamurty P, Trosnikov I, Nobre CA, Bonatti JP, Camargo H, Sampaio G, Sanches MB, Manzi AO, Castro CAC, D’Almeida C, Pezzi LP, Candido L (2003) Assessment of regional seasonal rainfall predictability using the CPTEC/COLA atmospheric GCM. Clim Dyn 21:459–475

    Article  Google Scholar 

  • Martin GM, Klingaman NP, Moise AF (2017) Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6.Geosci. Model Dev 10:105–126

    Article  Google Scholar 

  • Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys 20:851–875

    Article  Google Scholar 

  • Mizuta R, Yoshimura H, Murakami H, Matsueda M, Endo H, Ose T, Kamiguchi K, Hosaka M, Sugi M, Yukimoto S, Kusunoki S, Kitoh A (2012) Climate simulations using MRI-AGCM3.2 with 20-km grid. J Meteorol Soc Jpn 90A:233–258. https://doi.org/10.2151/jmsj.2012-A12

  • Morrison G, Curry JA, Khvorostyanov VI (2005) A new double-moment microphysics parameterization for application in cloud and climate models. Part I: description. J Atmos Sci 62:1665–1677. https://doi.org/10.1175/JAS3446.1

    Article  Google Scholar 

  • Morrison H, Thompson G, Tatarskii V (2009) Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes. Mon Weather Rev 137:991–1007

    Article  Google Scholar 

  • NMC Development Division Staff (1988) Research version of the medium range forecast model. NMC Documentation Series, pp 1339. Available from the Development Division, NCEP, Washington, DC 20233

  • Philander SGH (1985) El Niño and La Niña. J Atmos Sci 42:2652–2662

    Article  Google Scholar 

  • Philander SGH (1990) El Niño, La Niña, and the southern oscillation. Princeton University, Academic Press Inc, Princeton, New Jersey, p 291

    Google Scholar 

  • Rio C, Houdin F (2008) A thermal plume model for the convective boundary layer: representation of cumulus clouds. J Atmos Sci 65:407–425

    Article  Google Scholar 

  • Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with the El Nino/ Southern Oscillation. Mon Wea Rev 115:1606–1626

    Article  Google Scholar 

  • Ropelewski CF, Halpert MS (1989) Precipitation patterns associated with the high index phase of the Southern Oscillation. J Clim 2:268–284

    Article  Google Scholar 

  • Schiffer RA, Rossow WB (1983) The international satellite cloud climatology project (ISCCP): The first project of the world climate research programme. Bull Am Meteorol Soc 64:779–784

    Article  Google Scholar 

  • Souza DC, Kubota PY, Figueroa SN, Gutierrez EMAR, Coelho CAS (2019) Impacto da resolução horizontal na simulação dos jatos de baixos níveis na América do Sul usando o modelo global do CPTEC. Estudos Interdisciplinares nas Ciências Exatas e da Terra e Engenharias 4. E82ed.: Atena Editora, pp 205–217. https://doi.org/10.22533/at.ed.22519110919

  • Tarasova TA, Fomin BA (2000) Solar radiation absorption due to water vapor: advanced broadband parameterizations. J Appl Meteorol 39:1947–1951

    Article  Google Scholar 

  • Taylor KE, Williamson D, Zwiers F (2000) The sea surface temperature and sea ice concentration boundary conditions for AMIP II simulations PCMDI Report 60, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, pp 25

  • Toh YY, Turner AG, Johnson SJ, Holloway CE (2018) Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble. Clim Dyn 50(3–4):777–800. https://doi.org/10.1007/s00382-017-3641-x

    Article  Google Scholar 

  • Uppala SM et al (2005) The ERA-40 re-analysis. Q J Roy Meteor Soc 131:2961–3012

    Article  Google Scholar 

  • Velasco I, Fritsch JM (1987) Mesoscale convective com-plexes in the Americas. J Geophys Res 92:9591–9613

    Article  Google Scholar 

  • Waliser D, Sperber K, Hendon H, Kim D, Maloney E, Wheeler M, Weickmann K, Zhang C, Donner L, Gottschalck J, Higgins W, Kang I-S, Legler D, Moncrieff N, Schubert S, Stern W, Vitart F, Wang B, Wang W, Woolnough S (2009) MJO Simulation Diagnostics. J Clim 22:3006–3030

    Article  Google Scholar 

  • Wallace JM, Gutzler DS (1981) Teleconections in the geopotential height field during the Northern Hemisphere winter. Mon Wea Rev 109:785–812

    Google Scholar 

  • Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon Wea Rev 132:1917–1932

    Article  Google Scholar 

  • Yu H, Kaufman YJ, Chin M, Feingold G, Remer LA, Anderson TL, Balkanski Y, Bellouin N, Boucher O, Christopher S, DeCola P, Kahn R, Koch D, Loeb N, Reddy MS, Schulz M, Takemura T, Zhou M (2006) A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos Chem Phys 6:613–666

    Article  Google Scholar 

Download references

Acknowledgements

We thank two anonymous reviewers for providing valuable comments and suggestions that contributed to improving the quality of this manuscript. We thank Bárbara Yamada for the technical support in obtaining part of the observational datasets used in this study. This research was partially supported by the Climate Science for Services Partnership Brazil project (CSSP-Brazil) funded by the Newton Fund. CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), process 305206/2019-2, and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), process 2015/50687-8 (CLIMAX Project) for the support received. DCS was supported by CNPq (process 167804/2018-9). BSG was supported by CNPq and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). NPK was supported by an Independent Research Fellowship from the UK Natural Environment Research Council (NE/L010976/1) and by the National Centre for Atmospheric Science ACREW project.

Author information

Authors and Affiliations

  1. Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Rodovia Presidente Dutra, Km 40, SP-RJ, Cachoeira Paulista, SP, 12630-000, Brazil

    Caio A. S. Coelho, Dayana C. de Souza, Paulo Y. Kubota, Simone M. S. Costa, Layrson Menezes, Bruno S. Guimarães, Silvio N. Figueroa, José P. Bonatti, Iracema F. A. Cavalcanti & Gilvan Sampaio

  2. National Centre for Atmospheric Science–Climate and Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading, RG6 6BB, Berkshire, UK

    Nicholas P. Klingaman

  3. School of Earth and Environment, Institute for Climate and Atmospheric Science, University of Leeds, Leeds, UK

    Jessica C. A. Baker

Authors
  1. Caio A. S. Coelho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dayana C. de Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paulo Y. Kubota
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simone M. S. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Layrson Menezes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bruno S. Guimarães
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Silvio N. Figueroa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. José P. Bonatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Iracema F. A. Cavalcanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gilvan Sampaio
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nicholas P. Klingaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jessica C. A. Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caio A. S. Coelho.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6218 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Coelho, C.A.S., de Souza, D.C., Kubota, P.Y. et al. Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2. Clim Dyn 56, 873–898 (2021). https://doi.org/10.1007/s00382-020-05508-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-020-05508-8

Keywords

Search

Navigation