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The influence of South American regional climate on the simulation of the Southern Hemisphere extratropical circulation

  • Magdalena FalcoEmail author
  • Laurent Z. X. Li
  • Claudio G. Menéndez
  • Andrea F. Carril
Article

Abstract

This paper presents new modeling evidence showing the added value of high-resolution information from South America (SA) in the simulation of the Southern Hemisphere (SH) extratropical circulation. LMDZ, a coarse-resolution atmospheric global general circulation model constitutes the main tool for this investigation. Parallel to the control simulation, a two-way nesting (TWN) simulation of LMDZ is performed with an interactive coupling to the same model, but with a higher-resolution zoom over SA. The third simulation is a perfect boundary simulation for which re-analysis information from ERA-Interim is used to nudge LMDZ, but only over SA. Results indicate that enhanced resolution over SA improves the representation of the most important processes that influence extratropical eddy activity. The local improvement is followed by a better representation of the global extratropical circulation, especially in austral summer. The regional climate enhancement over SA has positive effects on simulation of the midlatitude jet position during the austral summer by significantly reducing the bias of the mean zonal kinetic energy outside the nudged zone. On the other hand, the wintertime general circulation outside the nudged-zone shows a limited bias-reduction for the regional-driven simulations, especially in the case of the TWN system. However, improvements of the TWN system compared to the control experiment are noticed in early stages of cyclone lifecycle, as it is identified in a better simulation of transient meridional heat transport and transient kinetic energy intensity. The findings of the present study suggest, thus, that improvements in resolution over SA effectively excite the simulation of the mean atmospheric circulation in the SH.

Keywords

Two-way nesting system Extratropical circulation Southern Hemisphere Influence of South American regional climate 

Notes

Acknowledgements

This work was supported by the Argentinean agencies CONICET (PIP 11220150100402CO) and ANPCyT (PICT 2014-0887, PICT-2015-3097), and by the French National Program LEFE/INSU (AO2015-876370). Computing resources were allocated by GENCI/IDRIS, the computer center of CNRS. The first author was supported by CONICET and by the Saint Exupéry Program (MED-MAEDI).

References

  1. Berbery EH, Barros VR (2002) The hydrologic cycle of the La Plata Basin in South America. J Hydrometeorol 3(6):630–645CrossRefGoogle Scholar
  2. Berckmans J, Woollings T, Demory M-E, Vidale P-L, Roberts M (2013) Atmospheric blocking in a high resolution climate model: influences of mean state, orography and eddy forcing. Atmos Sci Lett 14(1):34–40CrossRefGoogle Scholar
  3. Boville BA (1991) Sensitivity of simulated climate to model resolution. J Clim 4(5):469–485CrossRefGoogle Scholar
  4. Bracegirdle TJ, Shuckburgh E, Sallee J-B, Wang Z, Meijers AJS, Bruneau N, Phillips T, Wilcox LJ (2013) Assessment of surface winds over the Atlantic, Indian, and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: historical bias, forcing response, and state dependence. J Geophys Res 118(2):547–562Google Scholar
  5. Butterworth S (1930) On the theory of filter amplifiers. Wirel Eng 7(6):536–541Google Scholar
  6. Carril AF, Menendez C, Nunez M, Treut HL (2002) Mean flow-transient perturbation interaction in the Southern Hemisphere: a simulation using a variable-resolution GCM. Clim Dyn 18(8):661–673CrossRefGoogle Scholar
  7. Carril AF, Nuñez MN (2000) La respuesta de la circulación atmosférica en el Hemisferio Sur ante cambios prescritos en la temperatura de la superficie del mar extratropical. Atmósfera 13(1):39–51Google Scholar
  8. Ceppi P, Hwang Y-T, Frierson DMW, Hartmann DL (2012) Southern Hemisphere jet latitude biases in CMIP5 models linked to shortwave cloud forcing. Geophys Res Lett, 39(19):n/a–n/aGoogle Scholar
  9. Chang EKM, Guo Y, Xia X (2012) CMIP5 multimodel ensemble projection of storm track change under global warming. J Geophys Res, 117(D23):n/a–n/aGoogle Scholar
  10. Cunningham CA, Cavalcanti IF (2006) Intraseasonal modes of variability affecting the south atlantic convergence zone. Int J Climatol 26(9):1165–1180CrossRefGoogle Scholar
  11. Custodio MDS, da Rocha RP, Ambrizzi T, Vidale PL, Demory M-E (2017) Impact of increased horizontal resolution in coupled and atmosphere-only models of the HadGEM1 family upon the climate patterns of South America. Clim Dyn 48(9—-10):3341–3364CrossRefGoogle Scholar
  12. Custodio MDS, da Rocha RP, Vidale PL (2012) Analysis of precipitation climatology simulated by high resolution coupled global models over the South America. Hydrol Res Lett 6(0):92–97CrossRefGoogle Scholar
  13. De Sales F, Xue Y (2011) Assessing the dynamic-downscaling ability over South America using the intensity-scale verification technique. Int J Climatol 31(8):1205–1221CrossRefGoogle Scholar
  14. DeBlander E, Shaman J (2017) Teleconnection between the south atlantic convergence zone and the southern indian ocean: implications for tropical cyclone activity. J Geophys Res 122(2):728–740Google Scholar
  15. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  16. Edmon H Jr, Hoskins B, McIntyre M (1980) Eliassen-palm cross sections for the troposphere. J Atmos Sci 37(12):2600–2616CrossRefGoogle Scholar
  17. Figueroa SN, Satyamurty P, Da Silva Dias PL (1995) Simulations of the summer circulation over the South American region with an eta coordinate model. J Atmos Sci 52(10):1573–1584CrossRefGoogle Scholar
  18. Gozzo LF, da Rocha RP, Reboita MS, Sugahara S, Gozzo LF, Rocha RPD, Reboita MS, Sugahara S (2014) Subtropical cyclones over the Southwestern South Atlantic: climatological aspects and case study. J Clim 27(22):8543–8562CrossRefGoogle Scholar
  19. Grise KM, Polvani LM, Grise KM, Polvani LM (2014) Southern Hemisphere cloud-dynamics biases in CMIP5 models and their implications for climate projections. J Clim 27(15):6074–6092CrossRefGoogle Scholar
  20. Hoskins BJ, Hodges KI (2005) A new perspective on Southern Hemisphere storm tracks. J Clim 18(20):4108–4129CrossRefGoogle Scholar
  21. Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne J-L, Fairhead L, Filiberti M-A, Friedlingstein P, Grandpeix J-Y, Krinner G, LeVan P, Li Z-X, Lott F (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Clim Dyn 27(7–8):787–813CrossRefGoogle Scholar
  22. Inatsu M, Hoskins BJ (2004) The zonal asymmetry of the Southern Hemisphere winter storm track. J Clim 17(24):4882–4892CrossRefGoogle Scholar
  23. Inatsu M, Kimoto M (2009) A scale interaction study on East Asian cyclogenesis using a general circulation model coupled with an interactively nested regional model. Mon Weather Rev 137(9):2851–2868CrossRefGoogle Scholar
  24. Insel N, Poulsen CJ, Ehlers TA (2010) Influence of the Andes Mountains on South American moisture transport, convection, and precipitation. Clim Dyn 35(7):1477A–1492ACrossRefGoogle Scholar
  25. IPCC (2013). 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, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia. Cambridge University Press, pp 1535Google Scholar
  26. Iqbal W, Leung W, Hannachi A (2018) Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5. Clim Dyn 51:235–247CrossRefGoogle Scholar
  27. James I, Anderson D (1984) The seasonal mean flow and distribution of large-scale weather systems in the southern hemisphere: the effects of moisture transports. Q J R Meteorol Soc 110(466):943–966CrossRefGoogle Scholar
  28. Junquas C, Li L, Vera CS, Le Treut H, Takahashi K (2016) Influence of South America orography on summertime precipitation in Southeastern South America. Clim Dyn 46:3941–3963CrossRefGoogle Scholar
  29. Knippertz P, Wernly H, Glaser G (2013) A global climatology of tropical moisture exports. J Clim 26(10):3031–3045CrossRefGoogle Scholar
  30. Krinner G, Beaumet J, Favier V, Déqué M, Brutel-Vuilmet C (2018) Empirical run-time bias correction for antarctic regional climate projections with a stretched-grid agcm. J Adv Modeling Earth Syst 11(1):64–82CrossRefGoogle Scholar
  31. Laprise R (2008) Regional climate modelling. J Comput Phys 227(7):3641–3666CrossRefGoogle Scholar
  32. Lenters JD, Cook KH (1997) On the origin of the Bolivian high and related circulation features of the South American climate. J Atmos Sci 54(5):656–678CrossRefGoogle Scholar
  33. Li Z-X (1999) Ensemble atmospheric gcm simulation of climate interannual variability from 1979 to 1994. J Clim 12(4):986–1001CrossRefGoogle Scholar
  34. Lorenz EN (1955) Available potential energy and the maintenance of the general circulation. Tellus 7(2):157–167CrossRefGoogle Scholar
  35. Madonna E, Wernli H, Joos H, Martius O (2014) Warm conveyor belts in the ERA-interim dataset (1979–2010). Part I: climatology and potential vorticity evolution. J Clim 27(1):3–26CrossRefGoogle Scholar
  36. Mendes D, Souza EP, Trigo IF, Miranda PMA (2007) On precursors of South American cyclogenesis. Tellus A 59(1):114–121CrossRefGoogle Scholar
  37. Menendez C, Serafini V, Le Treut H (1999) The effect of sea-ice on the transient atmospheric eddies of the southern hemisphere. Clim Dyn 15(9):659–671CrossRefGoogle Scholar
  38. Menéndez CG, Saulo AC, Li Z-X (2001) Simulation of South American wintertime climate with a nesting system. Clim Dyn 17(2–3):219–231CrossRefGoogle Scholar
  39. Müller WA, Jungclaus JH, Mauritsen T, Baehr J, Bittner M, Budich R, Bunzel F, Esch M, Ghosh R, Haak H, Ilyina T, Kleine T, Kornblueh L, Li H, Modali K, Notz D, Pohlmann H, Roeckner E, Stemmler I, Tian F, Marotzke J (2018) A higher-resolution version of the max planck institute earth system model (MPI-ESM 1.2-HR). J Adv Modeling Earth Syst 10:1383–413CrossRefGoogle Scholar
  40. Nakamura H, Shimpo A (2004) Seasonal variations in the southern hemisphere storm tracks and jet streams as revealed in a reanalysis dataset. J Clim 17(9):1828–1844CrossRefGoogle Scholar
  41. Nielsen DM, Belém AL, Marton E, Cataldi M (2018) Dynamics-based regression models for the south atlantic convergence zone. Clim Dyn 1–27Google Scholar
  42. Pithan F, Shepherd TG, Zappa G, Sandu I (2016) Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag. Geophys Res Lett 43(13):7231–7240CrossRefGoogle Scholar
  43. Rickenbach TM, Ferreira RN, Halverson JB, Herdies DL, Dias MAS (2002) Modulation of convection in the southwestern amazon basin by extratropical stationary fronts. J Geophys Res 107(D20):LBA-7CrossRefGoogle Scholar
  44. Sakaguchi K, Leung LR, Zhao C, Yang Q, Lu J, Hagos S, Rauscher SA, Dong L, Ringler TD, Lauritzen PH (2015) Exploring a multiresolution approach Using AMIP simulations. J Clim 28(14):5549–5574CrossRefGoogle Scholar
  45. Sakaguchi K, Lu J, Leung LR, Zhao C, Li Y, Hagos S (2016) Sources and pathways of the upscale effects on the Southern Hemisphere jet in MPAS-CAAM4 variable-resolution simulations. J Adv Modeling Earth Syst 8(4):1786–1805CrossRefGoogle Scholar
  46. Salio P, Nicolini M, Saulo AC (2002) Chaco low-level jet events characterization during the austral summer season. J Geophys Res 107(D24):4816CrossRefGoogle Scholar
  47. Sandu I, van Niekerk A, Shepherd TG, Vosper SB, Zadra A, Bacmeister J, Brown AR, Dörnbrack A, McFarlane N et al (2019) Impacts of orography on large-scale atmospheric circulation. npj Clim Atmos Sci 2(1):10CrossRefGoogle Scholar
  48. Sardeshmukh PD, Hoskins BJ (1988) The generation of global rotational flow by steady idealized tropical divergence. J Atmos Sci 45(7):1228–1251CrossRefGoogle Scholar
  49. Seluchi ME, Marengo JA (2000) Tropical—midlatitude exchange of air masses during summer and winter in South America: climatic aspects and examples of intense events. Int J Climatol 20:1167–1190CrossRefGoogle Scholar
  50. Shaw TA, Baldwin M, Barnes EA, Caballero R, Garfinkel CI, Hwang Y-T, Li C, O’Gorman PA, Rivière G, Simpson IR, Voigt A (2016) Storm track processes and the opposing influences of climate change. Nat Geosci 9(9):656–664CrossRefGoogle Scholar
  51. Shimizu MH, Cavalcanti IF (2011) Variability patterns of rossby wave source. Clim Dyn 37(3–4):441–454CrossRefGoogle Scholar
  52. Sinclair MR (1995) A climatology of cyclogenesis for the Southern Hemisphere. Mon Weather Rev 123(6):1601–1619CrossRefGoogle Scholar
  53. Trenberth KE (1991) Storm tracks in the Southern Hemisphere. J Atmos Sci 48(19):2159–2178CrossRefGoogle Scholar
  54. Wilcox LJ, Charlton-Perez AJ, Gray LJ (2012) Trends in Austral jet position in ensembles of high- and low-top CMIP5 models. J Geophys Res, 117(D13):n/a–n/aGoogle Scholar
  55. Zappa G, Shaffrey LC, Hodges KI, Sansom PG, Stephenson DB (2013) A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. J Clim 26(16):5846–5862CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Centro de Investigaciones del Mar y la Atmósfera (CIMA)CONICET-UBA, Ciudad Universitaria, Ciudad Autónoma de Buenos AiresBuenos AiresArgentina
  2. 2.Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI3351-IFAECI/CNRS-CONICET-UBA)Buenos AiresArgentina
  3. 3.Universidad de Buenos Aires, Facultad de Ciencias Exactas y NaturalesBuenos AiresArgentina
  4. 4.Laboratoire de Météorologie Dynamique (LMD)CNRS, Sorbonne Université, École Normale Supérieure, École PolytechniqueParisFrance

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