Climate Dynamics

, Volume 43, Issue 5–6, pp 1221–1239 | Cite as

The surface radiation budget over South America in a set of regional climate models from the CLARIS-LPB project

  • Natalia L. Pessacg
  • Silvina A. Solman
  • Patrick Samuelsson
  • Enrique Sanchez
  • José Marengo
  • Laurent Li
  • Armelle Reca C. Remedio
  • Rosmeri P. da Rocha
  • Caroline Mourão
  • Daniela Jacob


The performance of seven regional climate models in simulating the radiation and heat fluxes at the surface over South America (SA) is evaluated. Sources of uncertainty and errors are identified. All simulations have been performed in the context of the CLARIS-LPB Project for the period 1990–2008 and are compared with the GEWEX-SRB, CRU, and GLDAS2 dataset and NCEP-NOAA reanalysis. Results showed that most of the models overestimate the net surface short-wave radiation over tropical SA and La Plata Basin and underestimate it over oceanic regions. Errors in the short-wave radiation are mainly associated with uncertainties in the representation of surface albedo and cloud fraction. For the net surface long-wave radiation, model biases are diverse. However, the ensemble mean showed a good agreement with the GEWEX-SRB dataset due to the compensation of individual model biases. Errors in the net surface long-wave radiation can be explained, in a large proportion, by errors in cloud fraction. For some particular models, errors in temperature also contribute to errors in the net long-wave radiation. Analysis of the annual cycle of each component of the energy budget indicates that the RCMs reproduce generally well the main characteristics of the short- and long-wave radiations in terms of timing and amplitude. However, a large spread among models over tropical SA is apparent. The annual cycle of the sensible heat flux showed a strong overestimation in comparison with the reanalysis and GLDAS2 dataset. For the latent heat flux, strong differences between the reanalysis and GLDAS2 are calculated particularly over tropical SA.


Regional climate models Surface radiation budget Heat fluxes South America Uncertainties 



The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement N° 212492 (CLARIS LPB-A Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin). The GEWEX/SRB data were obtained from NASA Langley Research Center Atmospheric Sciences Data Center NASA/GEWEX SRB Project. This paper also is a contribution of the Brazilian National Institute of Science and Technology (INCT) for Climate Change funded by CNPq Grant Number 573797/2008-0/FAPESP Grant Number 57719-9 and the RedeClima. This work has also been supported by UBACyT Grant Y028 and CONICET GrantsPIP 112-200801-00195 and PIP 112-201101-00189.


  1. Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: single columntests using GATE wave, BOMEX, and arctic air-mass data sets. Quart J Roy Meteor Soc 112:693–709Google Scholar
  2. Bony S, Emanuel KA (2001) A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J Atmos Sci 58(21):3158–3183CrossRefGoogle Scholar
  3. Chen F, Dudhia J (2001) Coupling and advanced land surface hydrology model with the PennState-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Wea Rev 129:569–585Google Scholar
  4. Chou SC, Marengo JA, Lyra A, Sueiro G, Pesquero J, Alves LM, Kay G, Betts R, Chagas D, Gomes JL, Bustamante J, Tavares P (2011) Downscaling of South America present climate driven by 4-member HadCM3 runs. Clim Dyn. doi: 10.1007/s00382-011-1002-8 Google Scholar
  5. Colas F, McWilliams JC, Capet X, Kurian J (2011) Heat balance and eddies in the Peru-Chile current system. Clim Dyn. doi: 10.1007/s00382-011-1170-6 Google Scholar
  6. Collins W, Bitz C, Blackmon M, Bonan G, Bretherton C, Carton J, Chang P, Doney S, Hack J, Henderson T, Kiehl J, Large W, Mckenna D, Santer B, Smith R (2006) The community climate system model version 3 CCSM. J Clim 19:2122–2143. doi: 10.1175/JCLI13761 CrossRefGoogle Scholar
  7. Culf A, Esteves J, Marques Filho A, da Rocha H (1996) Radiation, temperature and humidityover forest and pasture in Amazonia. In: Gash J, Nobre C, Roberts J, Victoria R (eds) Chapter 10, in Amazonian deforestation and climate. Wiley, Chichester, pp 175–192Google Scholar
  8. da Rocha HR et al. (2009a) Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil. J Geophys Res 114:G00B12. doi: 10.1029/2007JG000640
  9. da Rocha RP, Morales CA, Cuadra SV, Ambrizzi T (2009b) Precipitation diurnal cycle and summer climatology assessment over South America: an evaluation of regional climate model version 3 simulations. J Geophys Res 114:D10108. doi: 10.1029/2008JD010212
  10. de Elía R, Caya D, Côté H, Frigon A, Biner S, Giguère M, Paquin D, Harvey R, Plummer D (2008) Evaluation of uncertainties in the CRCM-simulated North American climate. Clim Dyn 30:113–132Google Scholar
  11. de Rosnay P, Polcher J (1998) Modeling root water uptake in a complex land surface scheme coupled to a GCM. Hydrol Earth Syst Sci 2:239–256CrossRefGoogle Scholar
  12. Déqué M, Rowell DP, Luthi D, Giorgi F, Christensen JH, Rockel B, Jacobson D, Kjellstrom E, de Castro M, van der Hurk B (2007) An intercomparison of regional climatic simulations for Europe: assessing uncertainties in model projections. Clim Change 81:53–70CrossRefGoogle Scholar
  13. Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphere–atmosphere transfer scheme (BATS) version 1E as coupled to the NCAR community climate model. NCAR Tech. note, NCAR/TN-387. National Center for Atmospheric Research, Boulder, COGoogle Scholar
  14. Domínguez M, Gaertner MA, de Rosnay P, Losada T (2010) A regional climate model simulation over West Africa: parameterization tests and analysis of land surface fields. ClimDyn 35:249–265. doi: 10.1007/s00382-010-0769-3 Google Scholar
  15. Dümenil L, Todini E(1992) A rainfall-runoff scheme for use in the Hamburg climate model. In: J.P. O’Kane (ed) (a tribute to James Dooge) Advances in theoretical hydrology. European geophysical society series on hydrological sciences, 1. Elsevier Press: Amsterdam, pp 129–157Google Scholar
  16. Ek MB, Mitchell KE, Lin Y, Rogers E, Grummann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational Mesoscale Eta model. J Geophys Res 108:8851. doi: 10.1029/2002JD003296 CrossRefGoogle Scholar
  17. Emanuel KA (1991) The theory of hurricanes. Ann Rev Fluid Mech 23:179–196CrossRefGoogle Scholar
  18. Emanuel KA (1993) A cumulus representation based on the episodic mixing model: the importance of mixing and microphysics in predicting humidity. AMS Meteorol Monographs 24(46):185–192Google Scholar
  19. FelsS Schwarzkopf M (1975) The simplified exchanged approximation-A new method for radiative transfer calculations. J Atmos Sci 32:1475–1488CrossRefGoogle Scholar
  20. Garand L (1983) Some improvements and complements to the infrared emissivity algorithm including a parameterization of the absorption in the continuum region. J Atmos Sci 40:230–244CrossRefGoogle Scholar
  21. Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull 58:175–183Google Scholar
  22. Grell GA, Dudhia J, Stauffer DR (1993) A description of the fifth generation PennSystem/NCAR mesoscale model (MM5). NCAR Tech Note NCAR/TN–398 + 1A, p 107Google Scholar
  23. Gupta SK, Ritchey NA, Wilber AC, Whitlock CH, Gibson GG, Stackhouse PW (1999) A climatology of surface radiation budget derived from satellite data. J Clim 12:2691–2710CrossRefGoogle Scholar
  24. Gupta SK, Stackhouse PW, Mikovitz JC, Cox SJ, Zhang T (2006) Surface radiation budget project completes 22-year data set. GEWEX WCRP News 16(4):12–13Google Scholar
  25. Hong S-Y, Dudhia J, Chen S-H (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  26. Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne JL, Fairhead L, Filiberti MA, Friedlingstein P, Grandpeix JY, Krinner G, Levan P, Li ZX, Lott F (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. ClimDyn 27(7–8):787–813Google Scholar
  27. Hsie EY, Anthes RA, Keyser D (1984) Numerical simulation of frontogenesis in a moist atmosphere. J AtmosSci 41:2581–2594CrossRefGoogle Scholar
  28. IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p 996Google Scholar
  29. Jacob D, Andrae U, Elgered G, Fortelius C, Graham LP, Jackson SD, Karstens U, Koepken C, Lindau R, Podzun R, Rockel B, Rubel F, Sass HB, Smith RND, Van den Hurk VJ, Yang X (2001) A comprehensive model intercomparison study investigating the water budget during the BALTEX-PIDCAP period. MeteorolAtmosPhys 77(1–4):19–43Google Scholar
  30. Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzum R, Rechid D, Remedio AR, Saeed F, Sieck K, Teichmann C, Wilhelm C (2012) Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere 3(1):181–199CrossRefGoogle Scholar
  31. Jaeger EB, Anders I, Lüthi D, Rockel B, Schär C, Seneviratne SI (2008) Analysis of ERA40driven CLM simulations for Europe. Meteorol Z 17(4):349–367CrossRefGoogle Scholar
  32. Janjic ZI (1994) The step-mountain eta coordinate model: further development of the convection, viscous sublayer, and turbulent closure schemes. Mon Wea Rev 122:927–945CrossRefGoogle Scholar
  33. Jones CG, Sanchez E (2002) The representation of shallow cumulus convection and associated cloud fields in the Rossby Centre Atmospheric Model. HIRLAM Newsletter 41. Available on request from SMHI, S601-76 Norrköping SwedenGoogle Scholar
  34. Kain JS (2004) The Kain–Fritsch Convective Parameterization: An Update. J Appl Meteor 43:170–181Google Scholar
  35. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J AtmosSci 47:2784–2802CrossRefGoogle Scholar
  36. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The representation of cumulus Convection in Numerical Models, Meteor Monogr No. 24, Amer Meteor Soc pp 165–170Google Scholar
  37. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DEO AMIP-II Reanalysis (R-2): Bull Amer Meteor Soc 1631–1643Google Scholar
  38. Kiehl JT, Bonan JJ, Boville BA, Briegleb BP, Williamson DL, Rasch PJ (1996) Description of the NCAR community climate model (CCM3). NCAR Tech. Note, NCAR/TN- 420 + STR, National Center for Atmospheric Research, Boulder, COGoogle Scholar
  39. Kothe S, Ahrens B (2010) On the radiation budget in regional climate simulations for West Africa. J Geophys Res 115:D23120. doi: 10.1029/2010JD014331 CrossRefGoogle Scholar
  40. Kothe S, Dobler A, Beck A, Ahrens B (2010) The radiation budget in a regional climate model. ClimDyn 36(5–6):1023–1036. doi: 10.1007/s00382-009-0733-2 Google Scholar
  41. Krinner G, Viovy N, de Noblet‐Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice IC (2005) A dynamic global vegetation model for studies of the coupled atmosphere‐ biosphere system, Global Biogeochem Cycles, 19, GB1015, doi: 10.1029/2003GB002199
  42. Lacis AA, Hansen JE (1974) Parameterization for the absorption of solar radiation in the Earth’s atmosphere. J Atm Sci 31:18–133Google Scholar
  43. Lenderink G, van Ulden A, van den Hurk B, van Meijgaard E (2007) Summertime inter-annual temperature variability in an ensemble of regional model simulations: analysis of the surface energy budget. Climatic Change 81:233–247. doi: 10.1007/s10584-006-9229-9 CrossRefGoogle Scholar
  44. Li Z (1999) Ensemble atmospheric GCM simulation of climate interannual variability from 1979 to 1994. J Climate 12:986–1001CrossRefGoogle Scholar
  45. Markovic M, Jones CG, Vaillancourt PA, Paquin D, Winger K, Paquin-Ricard D (2008) An evaluation of the surface radiation budget over North America for a suite of regional climate models against surface station observations. Clim Dyn 31(7–8):779–794Google Scholar
  46. Mitchell T, Jones P (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712. doi: 10.1002/joc.1181 CrossRefGoogle Scholar
  47. Morcrette J-J (1991) Radiation and cloud radiative properties in the ECMWF operational weather forecast model. J Geophys Res 96D:9121–9132CrossRefGoogle Scholar
  48. Nobre C, Fisch G, da Rocha H, Lyra R, da Rocha E, da Costa A, Ubarana V (1996) Observations of the atmospheric boundary layer in Rondonia. In: Gash J, Nobre C, Roberts J, Victoria R (eds) Chapter 24, in Amazonian deforestation and climate. Wiley, Chichester, pp 413–424Google Scholar
  49. Nordeng TE (1994) Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Research Department, Technical Memorandum No. 206, October 1994, p 41, European Centre for Medium Range Weather Forecasts, Reading, UKGoogle Scholar
  50. Pal JS, Small EE, Eltahir EAB (2000) Simulation of regional scale water and energy budgets: influence of a new moist physics scheme within RegCM. J Geophys Res 105:29579–29594CrossRefGoogle Scholar
  51. Pal JS, Giorgi F, Bi X, Elguindi N (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Am Meteorol Soc 88:1395–1409CrossRefGoogle Scholar
  52. Pesquero JF, Chou SC, Nobre CA, Marengo JA (2009) Climate downscaling over South America for 1961–1970 using the Eta Model. Theor Appl Climat. doi: 10.1007/s00704-009-0123-z Google Scholar
  53. Randall DA et al (2012) Intercomparison and interpretation of surface energy fluxes in atmospheric general circulation models. J Geophys Res 97(D4):3711–3724. doi: 10.1029/91JD03120 CrossRefGoogle Scholar
  54. Rash PJ, Kristjánsson JE (1998) A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J Climatol 11:1587–1614CrossRefGoogle Scholar
  55. Reboita MS, da Rocha RP, Ambrizzi T, Caetano E (2010) An assessment of the latent and sensible heat flux on the simulated regional climate over Southwestern South Atlantic ocean. Clim Dyn 34:873–889. doi: 10.1007/s00382-009-0681-x Google Scholar
  56. Rechid D, Raddataz T, Jacob D (2006) Influence of monthly varying vegetation on the simulated climate in Europe. MeteorologischeZeitschrift 15:99–116CrossRefGoogle Scholar
  57. Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng C-J, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin JK, Walker JP, Lohmann D, Toll D (2004) The global land data assimilation system. Bull Am Meteor Soc 85(3):381–394CrossRefGoogle Scholar
  58. Roeckner E, Arpe K, Bentsson L, Christoph M, Claussen M, Dümenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present day climate. Max-Planck institutfürmeteorologie report no. 218, p 90Google Scholar
  59. Samuelsson P, Gollvik S,Ullerstig A (2006) The land-surface scheme of the Rossby Centre regional atmospheric climate model (RCA3). Report in meteorology 122. SMHI, SE-60176 Norrköping, Sweden, p 25Google Scholar
  60. Samuelsson P, Jones Willén U, Ullerstig A, Gollvik S, Hansson U, JanssonC Kjellstrom E, Nikulin G, Wyser K (2011) The rossby centre regional climate model RCA3: model description and performance. Tellus Series A Dyn Meteorol Oceanogr 63(1):4–23CrossRefGoogle Scholar
  61. Sanchez E, Gaertner MA, Gallardo C, Padorno E, Arribas A, Castro M (2007) Impacts of a change in vegetation description on simulated European summer present-day and future climates. Clim Dyn 29:319–332Google Scholar
  62. Sass B H, Rontu L, Savijarvi HRaisanen P (1994) HIRLAM-2 Radiation scheme: documentation and tests. Hirlam technical report No. 16, SMHI, SE-60176 Norrkoping, Sweden, p 43Google Scholar
  63. Savijarvi H (1990) A fast radiation scheme for mesoscale model and short-range forecast models. J Appl Met 29:437–447CrossRefGoogle Scholar
  64. Simmons AS, Uppala DD, Kobayashi S (2007) ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsl 110:29–35Google Scholar
  65. Sitch S et al (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol 9:161–185CrossRefGoogle Scholar
  66. Solman SA, Pessacg NL (2011a) Regional climate simulations over South America: sensitivity to model physics and to the treatment of lateral boundary conditions using the MM5 model Clim Dyn. doi: 10.1007/s00382-011-1049-6
  67. Solman SA, Pessacg NL (2011b) Evaluating uncertainties in regional climate simulations over South America at the seasonal scale. Clim Dyn. doi: 10.1007/s00382-0111219-6 Google Scholar
  68. Solman S, Sanchez E, Samuelsson P, da Rocha R, Li L, Marengo J, Pessacg N, Remedio AR, Chou S, Berbery H, Le Treut H, de Castro M, Jacob D (2013) Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: models’ performance and uncertainties. Clim Dyn. doi: 10.1007/s00382-013-1667-2 Google Scholar
  69. Sörensson A, Menéndez C, Samuelsson P, Willén U, Hansson U (2010) Soil-precipitation feedbacks during the South American monsoon as simulated by a regional climate model. Clim Change 98:429–447CrossRefGoogle Scholar
  70. Stephens GL (1978) Radiation profiles in extended water clouds: II. Parameterization schemes. J Atmos Sci 35:2123–2132CrossRefGoogle Scholar
  71. Stephens GL (1984) The parameterization of radiation for numerical weather prediction and climate models. Mon Wea Rev 112:826–867CrossRefGoogle Scholar
  72. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large scale models. Mon Wea Rev 117:1779–1800CrossRefGoogle Scholar
  73. Trenberth KE, FasulloJT KJ (2009) Earth’s global energy budget. Bull Amer Meteor Soc 90:311–324CrossRefGoogle Scholar
  74. Wilber A, Smith L, Gupta S, Stackhouse P (2006) Annual cycles of surface shortwave radiative fluxes. J Climate 19:535–547CrossRefGoogle Scholar
  75. Wilks D (1995) Statistical Methods in the Atmospheric Sciences. Academic Press. Int Geophy Series vol 59Google Scholar
  76. Winter JM, Eltahir EAB (2011) Modeling the hydroclimatology of the midwestern United States. Part 1: current climate. Clim Dyn 38:573–593. doi: 10.1007/s00382-011-1182-2 Google Scholar
  77. Zhang Y, Rossow WB, Stackhouse PW (2006) Comparison of different global information sources used in surface radiative flux calculation: radiative properties of the near-surface atmosphere. J Geophys Res 111:D13106. doi: 10.1029/2005JD006873 CrossRefGoogle Scholar
  78. Zhang Y, Rossow WB, Stackhouse PW (2007) Comparison of different global information sources used in surface radiative flux calculation: radiative properties of the surface. J Geophys Res 112:D01102. doi: 10.1029/2005JD007008 Google Scholar
  79. Zhang T, Stackhouse PW, Gupta SK, Cox SJ, Mikovitz JC(2009) Validation and analysis of the release 3.0 of the NASA GEWEX surface radiation budget dataset. AIP Conf Proc 1100:597. doi:  10.1063/1.3117057
  80. Zhao Q, Black TL, Baldwin ME (1997) Implementation of the cloud prediction scheme in the Eta model at NCEP. Weather Forecast 12:697–712CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Natalia L. Pessacg
    • 1
  • Silvina A. Solman
    • 2
  • Patrick Samuelsson
    • 3
  • Enrique Sanchez
    • 4
  • José Marengo
    • 5
  • Laurent Li
    • 6
  • Armelle Reca C. Remedio
    • 7
  • Rosmeri P. da Rocha
    • 8
  • Caroline Mourão
    • 5
  • Daniela Jacob
    • 9
  1. 1.Centro Nacional Patagónico (CENPAT/CONICET)Puerto MadrynArgentina
  2. 2.Centro de Investigaciones Del Mar y la Atmósfera (CIMA/CONICET-UBA), DCAO/FCENUMI IFAECI/CNRSBuenos AiresArgentina
  3. 3.Rossby CentreSMHINorrköpingSweden
  4. 4.Facultad Ciencias Ambientales y BioquimicaUniversidad de Castilla-La ManchaToledoSpain
  5. 5.Centro de Ciencia do Sistema Terrestre-Instituto Nacional de Pesquisas Espaciais (CCST INPE)São PauloBrazil
  6. 6.Laboratoire de Météorologie Dynamique, IPSLCNRS/UPMCParisFrance
  7. 7.Max Planck Institute for MeteorologyHamburgGermany
  8. 8.Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências AtmosféricasUniversida de de São PauloSão PauloBrazil
  9. 9.Climate Service CenterHamburgGermany

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