Climatic Change

, Volume 156, Issue 4, pp 471–488 | Cite as

The impact of future urban scenarios on a severe weather case in the metropolitan area of São Paulo

  • Andréia BenderEmail author
  • Edmilson Dias Freitas
  • Luiz Augusto Toledo Machado


In this work, convective parameters are applied, based on numerical simulations made with Brazilian Regional Atmospheric Modeling System (BRAMS) model, to a severe weather case which occurred in the metropolitan area of São Paulo (MASP). Scenarios of future urban area growth and increase of building heights were made to evaluate changes in convective parameters and rainfall for the study region. Using factorial planning and factor separation methods, we found that the urban area growth predicted for 2030 is capable of increasing the amount of precipitation, mainly due to the land use change from rural to urban. In the scenario of building heights increasing, it was found a tendency for rainfall suppression. The urban area for 2030 is the major factor contributing to increasing atmospheric instability and wind shear. Vertical urban growth causes an increase in atmospheric instability and a decrease in wind shear. The interaction between urban area and building height factors increases the amount of precipitation and storm motion over the MASP.


Funding information

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) (Finance Code 001). The first author also acknowledges the National Council for Scientific and Technological Development (CNPq) for her scholarship. We also acknowledge the São Paulo Research Foundation for the financial support given this study (Processes number 2015/14497-0 and 2015/03804-9).


  1. Barros Neto B, Scarmino IS, Bruns RE (1995) Planejamento e otimização de experimentos, Campinas​: Editora da UNICAMP, 1995. 299 pp. ISBN 85-268-0336-0.Google Scholar
  2. Brooks HE, Doswell CA, Cooper J (1994) On the environments of tornadic and nontornadic mesocyclones. Weather Forecast 9(4):606–618.<0606:OTEOTA>2.0.CO;2 CrossRefGoogle Scholar
  3. Brooks HE, Lee JW, Craven JP (2003) The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos Res 67-68:73–94. CrossRefGoogle Scholar
  4. Carraça MGD, Collier CG (2007) Modelling the impact of high-rise buildings in urban areas on precipitation initiation. Meteorol Appl 14(2):149–161. CrossRefGoogle Scholar
  5. Carrió GG, Cotton WR, Cheng WYY (2010) Urban growth and aerosol effects on convection over Houston: part I: the August 2000 case. Atmos Res 96(4):560–574. CrossRefGoogle Scholar
  6. Chen C, Cotton WR (1983) A one-dimensional simulation of the stratocumulus-capped mixed layer. Bound-Layer Meteorol 25(3):289–321CrossRefGoogle Scholar
  7. Cotton WR, Anthes RA (1992) Storm and cloud dynamics. International Geophysics Series, Volume 44. Academic Press, 883 pp. eBook ISBN: 9780080959832Google Scholar
  8. Cotton WR, Pielke RA (1995) Human impacts on weather and climate. Cambridge University Press, 296 pp. ISBN-10: 052149592XGoogle Scholar
  9. Evans JS, Doswell CA (2001) Examination of derecho environments using proximity soundings. Weather Forecast 16(3):329–342.<0329:EODEUP>2.0.CO;2 CrossRefGoogle Scholar
  10. Freitas SR et al (2005) Monitoring the transport of biomass burning emissions in South America. Environ Fluid Mech 5(1–2):135–167. CrossRefGoogle Scholar
  11. Freitas ED, Rozoff CM, Cotton WR, Dias PLS (2007) Interactions of an urban heat island and sea-breeze circulations during winter over the metropolitan area of Sao Paulo, Brazil. Bound-Layer Meteorol 122(1):43–65. CrossRefGoogle Scholar
  12. Freitas ED et al., (2009a) Factors involved in the formation and development of severe weather conditions over the megacity of São Paulo, 89th American Meteorological Society meeting. AMS, Phoenix, AZGoogle Scholar
  13. Freitas SR et al (2009b) The coupled aerosol and tracer transport model to the Brazilian developments on the regional atmospheric modeling system (CATT-BRAMS) part 1: model description and evaluation. Atmos Chem Phys 9:2843–2861. CrossRefGoogle Scholar
  14. Galway JG (1956) The lifted index as a predictor of latent instability. Bull Am Meteorol Soc 37(10):528–529. CrossRefGoogle Scholar
  15. Grell GA, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters 29(14):38–1–38-4. CrossRefGoogle Scholar
  16. Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol 38(9):1262–1292.<1262:APOUAD>2.0.CO;2 CrossRefGoogle Scholar
  17. Haddad EA, Teixeira E (2015) Economic impacts of natural disasters in megacities: the case of floods in São Paulo, Brazil. Habitat International 45(part 2):106–113. CrossRefGoogle Scholar
  18. Johns RH, Doswell CA (1992) Severe local storms forecasting. Weather Forecast 7(4):588–612.<0588:SLSF>2.0.CO;2 CrossRefGoogle Scholar
  19. Khan S, Simpson R (2001) Effect of a heat island on the meteorology of a complex urban airshed. Bound-Layer Meteorol 100(3):487–506. CrossRefGoogle Scholar
  20. Masson V (2000) A physically-based scheme for the urban energy budget in atmospheric models. Bound-Layer Meteorol 94(3):357–397. CrossRefGoogle Scholar
  21. Mellor GL, Yamada T (1982) Development of a turbulence closure-model for geophysical fluid problems. Rev Geophys 20(4):851–875. CrossRefGoogle Scholar
  22. Meyers MP, Walko RL, Harrington JY, Cotton WR (1997) New RAMS cloud microphysics parameterization. Part II: the two-moment scheme. Atmospheric Research 45(1):3–39. CrossRefGoogle Scholar
  23. Mills GA, Colquhoun JR (1998) Objective prediction of severe thunderstorm environments: preliminary results linking a decision tree with an operational regional NWP model. Weather Forecast 13(4):1078–1092.<1078:OPOSTE>2.0.CO;2 CrossRefGoogle Scholar
  24. Mölders N, Olson MA (2004) Impact of urban effects on precipitation in high latitudes. J Hydrometeorol 5(3):409–429.<0409:IOUEOP>2.0.CO;2 CrossRefGoogle Scholar
  25. Moller AR (2001) Severe local storms forecasting. In: Doswell CA (ed) Severe convective storms. American Meteorological Society, Boston, pp 433–480. CrossRefGoogle Scholar
  26. Moncrieff MW, Miller MJ (1976) The dynamics and simulation of tropical cumulonimbus and squall lines. Q J R Meteorol Soc 102(432):373–394. CrossRefGoogle Scholar
  27. Nascimento E (2004) Identifying severe thunderstorm environments in southern Brazil: analysis of severe weather parameters, 22nd conference on severe local storms. American Meteorological Society, Hyannis, MA, EUA, pp. 8Google Scholar
  28. Pathirana A, Denekew HB, Veerbeek W, Zevenbergen C, Banda AT (2014) Impact of urban growth-driven landuse change on microclimate and extreme precipitation — a sensitivity study. Atmos Res 138:59–72. CrossRefGoogle Scholar
  29. PDE (2014). “Plano Diretor Estratégico do Município de São Paulo, LEI N° 16.050, DE 31 DE JULHO DE 2014, Diário Oficial, Suplemento, ano 59”, 1° de agosto de 2014, n° 140, 352 p. (In Portuguese). Available at Last access 30/05/2019
  30. Rozoff CM, Cotton WR, Adegoke JO (2003) Simulation of St. Louis, Missouri, Land Use Impacts on Thunderstorms. Journal of Applied Meteorology 42(6):716–738.<0716:SOSLML>2.0.CO;2 CrossRefGoogle Scholar
  31. Silva Dias MAF, Dias J, Carvalho LMV, Freitas ED, Silva Dias PL (2013) Changes in extreme daily rainfall for São Paulo, Brazil. Clim Chang 116(3–4):705–722. CrossRefGoogle Scholar
  32. Souza DO, Alvalá RCS, Nascimento MG (2016) Urbanization effects on the microclimate of Manaus: a modeling study. Atmos Res 167:237–248. CrossRefGoogle Scholar
  33. Stein U, Alpert P (1993) Factor separation in numerical simulations. J Atmos Sci 50(14):2107–2115.<2107:FSINS>2.0.CO;2 CrossRefGoogle Scholar
  34. Stensrud DJ, Cortinas JV, Brooks HE (1997) Discriminating between tornadic and nontornadic thunderstorms using mesoscale model output. Weather and Forecasting 12:613–632.;2 CrossRefGoogle Scholar
  35. Thompson RL (1998) Eta model storm-relative winds associated with tornadic and nontornadic supercells. Weather Forecast 13:125–137.<0125:EMSRWA>2.0.CO;2 CrossRefGoogle Scholar
  36. Thompson RL, Edwards R, Hart JA, Elmore KL, Markowski P (2003) Close proximity soundings within supercell environments obtained from the rapid update cycle. Weather Forecast 18(6):1243–1261.<1243:CPSWSE>2.0.CO;2 CrossRefGoogle Scholar
  37. Walko RL, Cotton WR, Meyers MP, Harrington JY (1995) New RAMS cloud microphysics parameterization part I: the single-moment scheme. Atmos Res 38(1):29–62. CrossRefGoogle Scholar
  38. Wieringa J (1993) Representative roughness parameters for homogeneous terrain. Bound-Layer Meteorol 63(4):323–363. CrossRefGoogle Scholar
  39. Young AF (2013) Urban expansion and environmental risk in the São Paulo metropolitan area. Clim Res 57(1):73–80CrossRefGoogle Scholar
  40. Zhong S, Yang X (2015) Ensemble simulations of the urban effect on a summer rainfall event in the great Beijing metropolitan area. Atmos Res 153:318–334. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências AtmosféricasUniversidade de São PauloSão PauloBrazil
  2. 2.Centro de Previsão de Tempo e Estudos ClimáticosInstituto Nacional de Pesquisas EspaciaisCachoeira PaulistaBrazil

Personalised recommendations