Climate Dynamics

, Volume 49, Issue 11–12, pp 3975–3987 | Cite as

Sensitivity of simulated South America climate to the land surface schemes in RegCM4

  • Marta Llopart
  • Rosmeri P. da Rocha
  • Michelle Reboita
  • Santiago Cuadra
Article

Abstract

This work evaluates the impact of two land surface parameterizations on the simulated climate and its variability over South America (SA). Two numerical experiments using RegCM4 coupled with the Biosphere–Atmosphere Transfer Scheme (RegBATS) and the Community Land Model version 3.5 (RegCLM) land surface schemes are compared. For the period 1979–2008, RegCM4 simulations used 50 km horizontal grid spacing and the ERA-Interim reanalysis as initial and boundary conditions. For the period studied, both simulations represent the main observed spatial patterns of rainfall, air temperature and low level circulation over SA. However, with regard to the precipitation intensity, RegCLM values are closer to the observations than RegBATS (it is wetter in general) over most of SA. RegCLM also produces smaller biases for air temperature. Over the Amazon basin, the amplitudes of the annual cycles of the soil moisture, evapotranspiration and sensible heat flux are higher in RegBATS than in RegCLM. This indicates that RegBATS provides large amounts of water vapor to the atmosphere and has more available energy to increase the boundary layer thickness and cause it to reach the level of free convection (higher sensible heat flux values) resulting in higher precipitation rates and a large wet bias. RegCLM is closer to the observations than RegBATS, presenting smaller wet and warm biases over the Amazon basin. On an interannual scale, the magnitudes of the anomalies of the precipitation and air temperature simulated by RegCLM are closer to the observations. In general, RegBATS simulates higher magnitude for the interannual variability signal.

Keywords

RegCM4 BATS CLM3.5 CORDEX Amazon basin Interannual variability 

Notes

Acknowledgments

The authors would like to acknowledge financial support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) - Brazil (Procs. 155700/2010-3, 249244/2013-6, 474929/2013-2, 474881/2013-0 and 307547/2014-0), and from Fapesp GoAmazon (Proc.2013/50521-7) and CAPES/PROEX. We thank the reviewers for their constructive and helpful comments and suggestions.

References

  1. Bonan GB (1996) A land surface model (LSM version1) for ecological, hydrological, and atmospheric studies: technical description and user’s guide. NCAR Technical Note NCAR/TN-417 + STR. National Center for Atmospheric Research, Boulder, CO, p 150Google Scholar
  2. Bruno RD, da Rocha HR, de Freitas HC, Goulden ML, Miller SD (2006) Soil moisture dynamics in an eastern Amazonian tropical forest. Hydrol Processes 20:2477–2489CrossRefGoogle Scholar
  3. Collins WD, Lee-Taylor JM, Edwards DP, Francis GL (2006b) Effects of increased near-infrared absorption by water vapor on the climate system. J. Geophys. Res., in pressGoogle Scholar
  4. da Rocha HR, Goulden ML, Miller S, Menton MC, Oliveira Pinto LDV, Freitas H, Figueira AMS (2004) Seasonality of water and heat fluxes over a tropical Forest in eastern Amazônia. Ecological Applications, S22–S32Google Scholar
  5. da Rocha HR, Manzi AO, Cabral OM, Miller SD, Goulden ML, Saleska SR, R.-Coupe N, Wofsy SC, Borma L et al (2009) 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 CrossRefGoogle Scholar
  6. da Rocha RP, Cuadra SV, Reboita MS, Kruger LF, Ambrizzi T, Krusche N (2012) Effects of RegCM3 parameterizations on simulated rainy season over South America. Clim Res 52:253–265Google Scholar
  7. da Rocha RP, Reboita MS, Llopart M (2016) A comparative analysis of the horizontal resolution impacts in simulated climate over South America. http://indico.ictp.it/event/7613/session/2/contribution/18/material/slides/0.pdf
  8. da Rocha RP, Reboita MS, Dutra LMM, Llopart M, Coppola E (2014) Climatic Change 125: 95. doi:10.1007/s10584-014-1119-y
  9. Dai Y, Zeng QC (1997) A land surface model (IAP94) for climate studies. Part I: formulation and validation in off-line experiments. Adv Atmos Sci 14:433–460Google Scholar
  10. de Gonçalves LGG, Borak JS, Costa MH, Saleska SR, Baker I et al (2013) Overview of the large-scale biosphere–atmosphere experiment in Amazonia Data Model Intercomparison Project (LBA-DMIP). Agric For Meteorol 182–183:111–127CrossRefGoogle Scholar
  11. de Jesus EM, da Rocha RP, Reboita MS, Llopart M, Mosso Dutra LM, Remedio ARC (2016) Contribution of cold fronts to seasonal rainfall in simulations over the southern La Plata Basin. Clim Res 68:243–255Google Scholar
  12. Deardorff J (1978) Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J Geophys Res 83:1889–1903CrossRefGoogle Scholar
  13. 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
  14. Dickinson RE, Henderson-Sellers A, Kennedy PJ, Wilson MF (1993) Biosphere–Atmosphere Transfer Scheme (BATS) version 1e as coupled to Community Climate Model. NCAR Tech. Note NCAR/TN-387 + STR, p 72Google Scholar
  15. Dirmeyer PA, Schlosser CA, Brubaker KL (2009b) Precipitation, recycling, and land memory: an integrated analysis. J Hydrometeorol 10(1):278–288. doi:10.1175/2008JHM1016.1
  16. Elguindi N, Bi X, Giorgi F, Nagarajan B, Pal J, Solmon F (2004) RegCM version 3.0 user’s guide. Trieste: PWCG Abdus Salam ICTP, p 48Google Scholar
  17. Emanuel KA, Zivkovic-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56:1766–1782CrossRefGoogle Scholar
  18. Fernandez JPR, Franchito SH, Rao VB (2006a) Simulation of summer circulation over South America by two regional climate models. Part I. Mean climatology. Theor Appl Climatol 86:247–260Google Scholar
  19. Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull 58:175–183Google Scholar
  20. Giorgi F, Coppola E, Solmon F et al (2012) RegCM4: Model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29CrossRefGoogle Scholar
  21. Grimm AM, Ambrizzi T (2009) Teleconnections into South America from the tropics and extratropics on interannual and intraseasonal timescales. In: Past climate variability in South America and surrounding regions: from the last glacial maximum to the Holocene. In: Vimeux F, Sylvestre F, Khodri M (eds) Developments in paleoenvironmental research. Springer, Netherlands, pp 159–191, doi:10.1007/978-90-481-2672-9
  22. Hartmann DL (1994) Global physical climatology. Academic Press, San DiegoGoogle Scholar
  23. Koster RD, Suarez MJ (2001) Soil moisture memory in climate models. J Hydrometeor 2:558–570CrossRefGoogle Scholar
  24. Koster RD et al (2004) Regions of strong coupling between soil moisture and precipitation. Science 305(5687):1138–1140CrossRefGoogle Scholar
  25. Koster R, Mahanama S, Yamada T, Balsamo G, Boisserie M, Dirmeyer P, Doblas-Reyes F, Gordon T, Guo Z, Jeong JH, Li Z, Luo L, Maleysev S, Merryfield W, Seneviratne SI, Stanelle T, van den Hurk B, Vitart F, Wood EF (2010) The contribution of land surface initialization to subseasonal forecast skill: first results from the GLACE-2 project. Geophys Res Lett 37:L02402CrossRefGoogle Scholar
  26. Koster RD, Mahama, a S, Yamada TJ, Balsamo G et al (2011) The second phase of the global land-atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. J Hydrometeorol 12:805–822CrossRefGoogle Scholar
  27. Legates DR, Willmott CJ (1990) Mean seasonal and spatial riability in gauge corrected, global precipitation. Int J Climatology 10:111–127CrossRefGoogle Scholar
  28. Llopart M, Coppola E, Giorgi F, da Rocha RP, Cuadra S. Climatic Change (2014) 125: 111. doi:10.1007/s10584-014-1140-1
  29. Marengo J (1992) Interannual variability of surface climate in the Amazon basin. Int J Climatol 12:853–863CrossRefGoogle Scholar
  30. Menendez CG et al (2010) claris project: towards climate downscaling in South America. Meteorol Z 19:357–362CrossRefGoogle Scholar
  31. Misra V, Dirmeyer PA, Kirtman BP (2002) Regional simulation of interannual variability over South America. J Geophys Res 107:8036. doi:10.1029/2001JD900216 CrossRefGoogle Scholar
  32. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatology 25:693–712CrossRefGoogle Scholar
  33. Nepstad DC, Carvalho CR de, Davidson EA, Jipp PH, Lefebvre PA, Negreiros GH, Silva ED da, Stone TA, Trumbore SE, Vieira S (1994) The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature 372:66–669CrossRefGoogle Scholar
  34. Oleson KW, Niu G, Yang ZL, Lawrence DM et al (2008) Improvements to the community land model and their impact on the hydrologic cycle. J Geophys Res 113:G01021. doi:10.1029/2007JD000563 CrossRefGoogle Scholar
  35. Orlowsky B, Seneviratne SI (2010) Statistical analyses of land atmosphere feedbacks and their possible pitfalls. J Clim 23:3918–3932CrossRefGoogle Scholar
  36. Pal JS et al (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Am Meteorol Soc 88:1395–1409CrossRefGoogle Scholar
  37. Pessacg NL, Solman SA, Samuelsson P, Sanchez E, Marengo J, Li L, Remedio ARC, Rocha RPd, Mourão C, Jacob D (2013) The surface radiation budget over South America in a set of regional climate models from the CLARIS-LPB project. Clim Dyn. doi:10.1007/s00382-013-1916-4
  38. Rauscher SA, Seth A, Liebmann B, Qian J-H, Camargo SJ (2007) Regional climate model–simulated timing and character of seasonal rains in South America. Mon Wea Rev 135:2642–2657.Google Scholar
  39. Reboita MS, da Rocha RP, Ambrizzi T, Sugahara S (2010) South Atlantic Ocean cyclogenesis climatology simulated by regional climate model (RegCM3). Clim Dyn 35:1331–1347Google Scholar
  40. Reboita MS, Fernandez JPR, Llopart MP, Rocha RP, Pampuch LA, Cruz FT (2014) Assessment of RegCM4.3 over the CORDEX South America domain: sensitivity analysis for physical parameterization schemes. Clim Res 60:215–234Google Scholar
  41. Ronchail J, Cochonneau G, Molinier M, Guyot JL, Gorreti A, Guimarães V, de Oliveira E (2002) Interannual rainfall variability in the Amazon basin and sea surface temperatures in the equatorial Pacific and the tropical Atlantic Oceans. Int J Climatol 22:1663–1686CrossRefGoogle Scholar
  42. Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with El Niño/Southern Oscillation. Mon Wea Rev 115:1606–1626CrossRefGoogle Scholar
  43. Ropelewski CF, Halpert MS (1989) Precipitation patterns associated with the high index phase of the Southern Oscillation. J Clim 2:268–284CrossRefGoogle Scholar
  44. Saleska SR, Didan K, Huete A, da Rocha HR (2007) Amazon forests green-up during 2005 drought. Science 318(5850):612CrossRefGoogle Scholar
  45. Sellers PJ, Shuttleworth WJ, Dorman J (1989) Calibrating the simple biosphere model for amazonian tropical forest using field and remote sensing data. Part I: Average calibration with field data. J Appl Meteorol 28:727–759CrossRefGoogle Scholar
  46. Sellers PJ, Randall DA, Collatz CJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD (1996a) A revised land surface parameterization (SiB2) for atmospheric GCMs, Part I: Model formulation. J Climate 9:676–705CrossRefGoogle Scholar
  47. Sellers PJ, Dickinson RE, Randall DA, Betts AK, Hall FG, Berry JA, Collatz GJ, Denning AS, Mooney HA, Nobre CA, Sato N, Field CB, Henderson-Sellers A (1997) Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science 275(5299):502–509CrossRefGoogle Scholar
  48. Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land atmosphere coupling and climate change. Eur Nat 443:205–209CrossRefGoogle Scholar
  49. Seth A, Rauscher SA, Carmago SJ, Qian JH, Pal JS (2007) RegCM3 regional climatologies using reanalysis and ECHAM global model driving fields. Climate Dyn 28:461–480CrossRefGoogle Scholar
  50. Silva VBS, Kousky VE, Shi W, Higgins RW (2007) An improved historical daily precipitation analysis for Brazil. J Hydrometeorol 8:847–861Google Scholar
  51. Silva MES, Pereira G, da Rocha RP (2015) Theor Appl Climatol 125:609. doi:10.1007/s00704-015-1516-9 CrossRefGoogle Scholar
  52. Solman SA, Sanchez E, Samuelsson P, da Rocha RP, Li L, Marengo J, Pessacg NL, Remedio ARC, Chou SC, 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: model performance and uncertainties. Clim Dyn 41(5–6):1139–1157CrossRefGoogle Scholar
  53. Sörensson AA, Menéndez CG (2011) Summer soil-precipitation coupling in South America. Tellus Ser A Dyn Meteorol Oceanogr 63:56–68CrossRefGoogle Scholar
  54. Sörensson AA, Menéndez CG, 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
  55. Steiner AL, Pal JS, Giorgi F, Dickinson RE, Chameides WL (2005) Coupling of the common land model (CLM0) to a regional climate model (RegCM). Theor Appl Climatol 82(3–4):225–243CrossRefGoogle Scholar
  56. Steiner AL, Pal JS, Rauscher SA, Bell JL et al (2009) Land surface coupling in regional climate simulations of the West Africa monsoon. Clim Dyn 33(6):869–892CrossRefGoogle Scholar
  57. Stieglitz M, Rind D, Famiglietti J, Rosenzweig C (1997) An efficient approach to modeling the topographic control surface hydrology for regional and global climate modeling. J Clim 10:118–137CrossRefGoogle Scholar
  58. Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J et al (2013) IPCC 2013 Summary for Policymakers. 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. Cambridge University Press, CambridgeGoogle Scholar
  59. Tawfik AB, Steiner AL (2011) The role of soil ice in land–atmosphere coupling over the United States: a soil moisture precipitation winter feedback mechanism. J Geophys Res 116:D02113CrossRefGoogle Scholar
  60. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Amer Meteor Soc 79:61–78CrossRefGoogle Scholar
  61. Uvo CRB, Repelli CA, Zebiak S, Kushnir Y (1998) The relationship between tropical Pacific and Atlantic SST and northeast Brazil monthly precipitation. J Climate 11:551–562CrossRefGoogle Scholar
  62. Vera C et al (2006) Toward a unified view of the American monsoon systems. J Climate 19:4977–5000CrossRefGoogle Scholar
  63. Wilks DS (1995) Statistical Methods in the atmospheric sciences: an introduction. Academic Press, p 467Google Scholar
  64. Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Amer Meteor Soc 78:2539–2558CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Departamento de FísicaUniversidade Estadual Paulista (UNESP)BauruBrazil
  2. 2.Centro de Meteorologia de Bauru (IPMet)BauruBrazil
  3. 3.Departamento de Ciências AtmosféricasUniversidade de São Paulo (USP)São PauloBrazil
  4. 4.Natural Resources InstituteFederal University of ItajubáItajubáBrazil
  5. 5.Brazilian Agricultural Research Corporation-EMBRAPACampinasBrazil

Personalised recommendations