Summer seasonal predictability of warm days in Argentina: statistical model approach

  • Soledad CollazoEmail author
  • Mariana Barrucand
  • Matilde Rusticucci
Original Paper


Predicting extreme temperature events can be very useful for different sectors that are strongly affected by their variability. The goal of this study is to analyze the influence of the main atmospheric, oceanic, and soil moisture forcing on the occurrence of summer warm days and to predict extreme temperatures in Argentina northern of 40°S by fitting a statistical model. In a preliminary analysis, we studied trends and periodicities. Significant positive trends, fundamentally in western Argentina, and two main periodicities of summer warm days were detected: 2–4 years and approximately 8 years. Lagged correlations allowed us to identify the key predictors: El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Standardized Precipitation Indices (SPI). We also noticed that the frequency of warm days in spring acts as a good predictor of summer warm days. Due to the collinearity among many predictors, principal component regression was used to simulate summer warm days. We obtained negative biases (i.e., the model tends to underestimate the frequency of summer warm days), but the observed and simulated values of summer warm days were significantly correlated, except in northwest Argentina. Finally, we analyzed the predictability of the summer warm days under ENSO neutral conditions, and we found new predictors: the geopotential height gradient in 850 hPa (between the Atlantic Anticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation (AMO), while the PDO and SPI lost some relevance.



This research was supported by CONICET PIP 0137-Res 4248/16 and UBACyT 2018 20020170100357BA. We want to thank the National Weather Service of Argentina and National Institute of Agricultural Technology for providing the data for this study. The authors want to especially thank Dr. Mariela Sued and Dr. Ana Bianco for her collaboration.

Supplementary material

704_2019_2933_MOESM1_ESM.docx (369 kb)
ESM 1 (DOCX 368 kb)
704_2019_2933_MOESM2_ESM.docx (32 kb)
ESM 2 (DOCX 32 kb)


  1. Abdul-Wahab SA, Bakheit CS, Al-Alawi SM (2005) Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ Model Softw 20:1263–1271. CrossRefGoogle Scholar
  2. Aceituno P (1988) On the functioning of the Southern Oscillation in the South American sector. Part I: Surface climate. Mon Weather Rev 116:505–524CrossRefGoogle Scholar
  3. Agosta EA, Compagnucci RH (2008) The 1976/77 austral summer climate transition effects on the atmospheric circulation and climate in southern South America. J Clim 21(17):4365–4383. CrossRefGoogle Scholar
  4. Alessandro AP (2014) Incidence and trend of blocking action situations on the temperature and precipitation in Argentina. Atmosfera 27(2):141–163. CrossRefGoogle Scholar
  5. Alexander MA, Kilbourne KH, Nye JA (2014) Climate variability during warm and cold phases of the Atlantic Multidecadal Oscillation (AMO) 1871–2008. J Mar Syst 113:14–26. CrossRefGoogle Scholar
  6. Al-lami AM, AM AL-S, YK AL-T (2017) Parameterization of the downward long wave radiation under clear-sky condition in Baghdad, Iraq. Asian J Appl Sci 10:10–17. CrossRefGoogle Scholar
  7. Allen MP (1997) The problem of multicollinearity. Understanding regression analysis. Springer, Boston, MA. CrossRefGoogle Scholar
  8. Alvarez MS, Vera CS, Kiladis GN, Liebmann B (2016) Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America. Clim Dyn 46:245–262. CrossRefGoogle Scholar
  9. Ashok K, Behera SK, Rao SA, Weng H, Yamagata T (2007) El Niño Modoki and its possible teleconnection. J Geophys Res 112:C11007. CrossRefGoogle Scholar
  10. Atlas R, Wolfson N, Terry J (1993) The effect of SST and soil moisture anomalies on GLA model simulations of the 1988 U.S. summer drought. J Clim 6:2034–2048.<2034:TEOSAS>2.0.CO;2 CrossRefGoogle Scholar
  11. Baliunas S, Frick P, Sokoloff D, Soon W (1997) Time scales and trends in the Central England temperature data (1959–1990): a wavelet analysis. Geophys Res Lett 24(11):1351–1354. CrossRefGoogle Scholar
  12. Barrett HG, Jones JM, Bigg GR (2018) Reconstructing El Niño Southern Oscillation using data from ships’ logbooks, 1815–1854. Part I: methodology and evaluation. Clim Dyn 50:845–862. CrossRefGoogle Scholar
  13. Barros V, Grimm A, Doyle M (2002) Relationship between temperature and circulation in Southeastern South America and its influence from El Nino and La Nina Events. J Meteorol Soc Jpn 80(1):21–32CrossRefGoogle Scholar
  14. Barrucand M, Rusticucci M, Vargas W (2008) Temperature extremes in the south of South America in relation to Atlantic Ocean Surface temperature and Southern Hemisphere circulation. J Geophys Res Atmos 113:D20111. CrossRefGoogle Scholar
  15. Barrucand MG, Zitto ME, Piotrkowski R, Canziani P, O’Neill A (2018) Historical SAM index time series: linear and nonlinear analysis. Int J Climatol 38:1091–1106. CrossRefGoogle Scholar
  16. Best DJ, Roberts DE (1975) Algorithm AS 89: the upper tail probabilities of Spearman’s Rho. J Royal Stat Soc, Series C (Applied Statistics) 24(3):377–379. CrossRefGoogle Scholar
  17. Biswas HR, Kundu PK (2018) A principal component analysis based model to predict post-monsoon tropical cyclone activity in the Bay of Bengal using oceanic Niño index and dipole mode index. Int J Climatol 38:2415–2422. CrossRefGoogle Scholar
  18. Brownlee J (2016) Master machine learning algorithms. Accessed 22 January 2018.
  19. Bunea F, She Y, Ombao H, Gongvatana A, Devlin K, Cohen R (2011) Penalized least squares regression methods and applications to neuroimaging. NeuroImage 55(4):1519–1527. CrossRefGoogle Scholar
  20. Buraga-Lefebvre C, Coëtmellec S, Lebrun D, Özkul C (2000) Application of wavelet transform to hologram analysis: three-dimensional location of particles. Opt Lasers Eng 33(6):409–421. CrossRefGoogle Scholar
  21. Butler MR, McNertney EM (1991) Estimating educational production functions: the problem of multicollinearity. Soc Sci J 28(4):489–499, ISSN 0362–3319. CrossRefGoogle Scholar
  22. Çelik K (2018) Predicting chlorophyll-a concentrations in two temperate reservoirs with different trophic states using principal component regression (PCR). Oceanol Hydrobiol Stud 47(1):1–9. CrossRefGoogle Scholar
  23. Cerne B, Vera CS (2010) Influence of the intraseasonal variability on heat waves in subtropical South America. Clim Dyn 36:2265–2277. CrossRefGoogle Scholar
  24. de Guenni LB, García M, Muñoz ÁG, Santos JL, Cedeño A, Perugachi C, Castillo J (2016) Predicting monthly precipitation along coastal Ecuador: ENSO and transfer function models. Theor Appl Climatol 129:1059–1073. CrossRefGoogle Scholar
  25. Doghmane H, Bourouba H, Messaoudi K, Bouridane A (2018) Palmprint recognition based on discriminant multiscale representation. J Electron Imaging 27(5):053032. CrossRefGoogle Scholar
  26. Doss-Gollin J, Muñoz X, Mason S, Pastén M (2018) Heavy rainfall in Paraguay during the 2015–2016 austral summer: causes and subseasonal-to-seasonal predictive skill. J Clim 31:6669–6685. CrossRefGoogle Scholar
  27. Doyle ME, Barros VR (2002) Midsummer low-level circulation and precipitation in subtropical South America and related sea surface temperature anomalies in the South Atlantic. J Clim 15:3394–3411.<3394:MLLCAP>2.0.CO;2 CrossRefGoogle Scholar
  28. Edwards DC, McKee TB (1997) Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report Number 97–2. Colorado State University, Fort Collins.Google Scholar
  29. Elsanabary MH, Gan TY (2014) Wavelet analysis of seasonal rainfall variability of the upper blue Nile basin, its teleconnection to global sea surface temperature, and its forecasting by an artificial neural network. Mon Weather Rev 142(5):1771–1791. CrossRefGoogle Scholar
  30. Enfield DB, Mestas AM, Mayer DA, Cid-Serrano L (1999) How ubiquitous is the dipole relationship in tropical Atlantic sea surface temperatures? J Geophys Res Oceans 104:7841–7848. CrossRefGoogle Scholar
  31. Enfield DB, Mestas-Nunez AM, Trimble PJ (2001) The Atlantic Multidecadal Oscillation and its relationship to rainfall and river flows in the continental U.S. Geophys Res Lett 28:2077–2080. CrossRefGoogle Scholar
  32. Fischer EM, Seneviratne S, Lüthi D, Schär C (2007a) Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys Res Lett 34:L06707.
  33. Fischer EM, Seneviratne S, Vidale P, Lüthi D, Schär C (2007b) Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J Clim 20:5081–5099.
  34. Foufoula-Georgiou E, Kumar P (1995) Wavelets in geophysics. Academic Press, 373 ppGoogle Scholar
  35. Frantziskonis G, Deymier P (2003) Wavelet-based spatial and temporal multiscaling: bridging the atomistic and continuum space and time scales. Phys Rev B 68(2):024105. CrossRefGoogle Scholar
  36. Garreaud RD, Vuille M, Compagnucci R, Marengo J (2009) Present-day South American Climate. Palaeogeogr Palaeoclimatol Palaeoecol 281:180–195. CrossRefGoogle Scholar
  37. Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Wiley, NYGoogle Scholar
  38. Gillett NP, Kell TD, Jones PD (2006) Regional climate impacts of the southern annular mode. Geophys Res Lett 33:L23704. CrossRefGoogle Scholar
  39. González MH, Garbarini E, Romero P (2015) Rainfall patterns and the relation to atmospheric circulation in northern Patagonia (Argentina). Adv Environ Res 41:85–100Google Scholar
  40. González MH, Garbarini E, Rolla AL, Eslamian S (2016) Meteorological drought indices: rainfall prediction in Argentina. In: Handbook of Drought and Water Scarcity. Vol. 1, Principle of Drought and Water Scarcity, Chapter 29, 540–567, Taylor & Francis Publishing (CRC Group) Editor: Saeid Eslamian. ISBN: 9781498731089 1498731082. Reino Unido, Abingdon.Google Scholar
  41. Grimm AM (2003) The El Niño impact on summer monsoon in Brazil: regional processes versus remote influences. J Clim 16:263–280CrossRefGoogle Scholar
  42. Grimm AM (2004) How do La Niña events disturb the summer monsoon system in Brazil? Clim Dyn 22:123–138CrossRefGoogle Scholar
  43. Grimm AM, Ferraz SET, Gomes J (1998) Precipitation anomalies in southern Brazil associated with El Niño and La Niña events. J Clim 11:2863–2880CrossRefGoogle Scholar
  44. Grimm AM, Barros VR, Doyle ME (2000) Climate variability in southern South America associated with El Niño and La Niña events. J Clim 13:35–58CrossRefGoogle Scholar
  45. Hannart A, Vera CS, Otto FEL, Cerne B (2015) Causal influence of anthropogenic forcings on the Argentinian heat wave of December 2013. Bull Amer Meteor Soc 96(12):S41–S45. CrossRefGoogle Scholar
  46. Hastie T, Tibshirani R, Friedman J (2010) The elements of statistical learning, second edition: data mining, inference, and prediction. Springer Series in Statistics. Retrieved from
  47. Hirschi M, Seneviratne SI, Alexandrov V, Boberg F, Boroneant C, Christensen OB, Formayer H, Orlowsky B, Stepanek P (2011) Observational evidence for soil moisture impact on hot extremes in southeastern Europe. Nat Geosci 4:17–21. CrossRefGoogle Scholar
  48. IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change - Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (Eds.) Available from Cambridge University Press, The Edinburgh Building, Shaftesbury Road, Cambridge CB2 8RU ENGLAND, 582 pp.Google Scholar
  49. Jackson E (1991) A user’s guide to principal components. Wiley, New YorkCrossRefGoogle Scholar
  50. Jaeger EB, Seneviratne SI (2011) Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Clim Dyn 36:1919–1939.}Google Scholar
  51. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer, New YorkCrossRefGoogle Scholar
  52. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77: 437–472. 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2Google Scholar
  53. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkCrossRefGoogle Scholar
  54. Kayano MT, Andreoli RV (2007) Relations of South American summer rainfall interannual variations with the Pacific Decadal Oscillation. Int J Climatol 27:531–540. CrossRefGoogle Scholar
  55. Kayano MT, Sansígolo C (2009) Interannual to decadal variations of precipitation and daily maximum and daily minimum temperatures in southern Brazil. Theor Appl Climatol 97:81–90. CrossRefGoogle Scholar
  56. Kendall MG (1975) Rank Correlation Methods, 4th edn. Charles Griffin, LondonGoogle Scholar
  57. Kestin TS, Karoly DJ, Yano J, Rayner NA (1998) Time–frequency variability of ENSO and stochastic simulations. J Clim 11: 2258–2272.<2258:TFVOEA>2.0.CO;2
  58. Kirby JF, Swain CJ (2013) Power spectral estimates using two-dimensional Morlet-fan wavelets with emphasis on the long wavelengths: jackknife errors, bandwidth resolution and orthogonality properties. Geophys J Int 194(1):78–99. CrossRefGoogle Scholar
  59. Kousky VE (1988) Pentad outgoing longwave radiation climatology for the South American sector. Rev bras meteorol 3:217–231Google Scholar
  60. Kumar S, Srivastava A (2012) Bootstrap prediction intervals in non-parametric regression with applications to anomaly detection. Conference Paper. The 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; 12–16 Aug. 2012; Beijing; China.
  61. Le T (2017) Use of the Morlet mother wavelet in the frequency-scale domain decomposition technique for the modal identification of ambient vibration responses. Mech Syst Signal Process 95:488–505. CrossRefGoogle Scholar
  62. Liebmann B, Kiladis GN, Vera CS, Saulo AC, Carvalho LMV (2004) Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone. J Clim 17:3829–3842.<3829:SVORIS>2.0.CO;2 CrossRefGoogle Scholar
  63. Lorenz R, Jaeger EB, Seneviratne SI (2010) Persistence of heat waves and its link to soil moisture memory. Geophys Res Lett 37(9):L09703. CrossRefGoogle Scholar
  64. Mann HB (1945) Non-parametric tests against trend. Econometrica 13:163–171CrossRefGoogle Scholar
  65. Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Amer Meteor Soc 78:1069–1079.<1069:APICOW>2.0.CO;2 CrossRefGoogle Scholar
  66. Marengo JA, Soares WR, Saulo C, Nicolini M (2004) Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: characteristics and temporal variability. J Clim 17:2261–2280.\2261:COTLJE[2.0.CO;2Google Scholar
  67. Marshall G (2003) Trends in the Southern Annular Mode from observations and reanalyses. J Clim 16:4134–4143.;2
  68. McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California, 17–22 January 1993. American Meteorological Society, Boston, pp 179–184Google Scholar
  69. McKee TB, Doesken NJ, Kleist J (1995) Drought monitoring with multiple time scales. Ninth Conference on Applied Climatology, American Meteorological Society, Jan 15–20, 1995, Dallas TX, pp.233–236.Google Scholar
  70. McLeod AI (2011) Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2:2 Google Scholar
  71. Mendes MCD, Trigo RM, Cavalcanti IFA, DaCamara CC (2008) Blocking episodes in the Southern Hemisphere: impact on the climate of adjacent continental areas. Pure Appl Geophys 165(9–10):1941–1962. CrossRefGoogle Scholar
  72. Mueller B, Seneviratne SI (2012) Hot days induced by precipitation deficits at the global scale. Proc Natl Acad Sci 109(31):12398–12403. CrossRefGoogle Scholar
  73. Nageswararao MM, Mohanty UC, Osuri KK, Ramakrishna SSVS (2016) Prediction of winter precipitation over northwest India using ocean heat fluxes. Clim Dyn 47:2253–2271. CrossRefGoogle Scholar
  74. Nair A, Mohanty UC, Acharya N (2013) Monthly prediction of rainfall over India and its homogenous zones during monsoon season: a supervised principal component regression approach on general circulation model products. Theor Appl Climatol 111:327–339. CrossRefGoogle Scholar
  75. National Academies of Sciences, Engineering, and Medicine (2016) Next generation earth system prediction: strategies for subseasonal to seasonal forecasts. The National Academies Press, Washington DC. CrossRefGoogle Scholar
  76. Naumann G, Vargas WM (2012) A study of intraseasonal temperature variability in southeastern South America. J Clim 25:5892–5903. CrossRefGoogle Scholar
  77. NOAA National Centers for Environmental Information, State of the Climate: Global Climate Report for Annual 2013, published online January 2014, retrieved on May 31, 2018 from Accessed 19 June 2018.
  78. Oglesby RJ, Erickson III DJ (1989) Soil moisture and the persistence of North American drought. J Clim 2: 1362–1380. doi:10.1175/1520-0442(1989)002,1362: SMATPO.2.0.CO;2.Google Scholar
  79. Osman M, Vera CS (2016) Climate predictability and prediction skill on seasonal time scales over South America from CHFP models. Clim Dyn 49:2365–2383. CrossRefGoogle Scholar
  80. Pal S, De Wekker SF, Emmitt GD (2016) Investigation of the spatial cariability of the convective boundary layer heights over an isolated mountain: cases from the MATERHORN-2012 experiment. J Appl Meteorol Climatol 55:1927–1952. CrossRefGoogle Scholar
  81. Pasquini AI, Lecomte KL, Piovano EL, Depetris PJ (2006) Recent rainfall and runoff variability in central Argentina. Quat Int 158(1):127–139. CrossRefGoogle Scholar
  82. Percival DP (1995) On estimation of the wavelet variance. Biometrika 82:619–631CrossRefGoogle Scholar
  83. Perrier V, Philipovitch T, Basdevant C (1995) Wavelet spectra compared to Fourier spectra. J Math Phys 36:1506–1519CrossRefGoogle Scholar
  84. Pozo-Vázquez D, Esteban-Parra M, Rodrigo F, Castro-Díez Y (2001) A study of NAO variability and its possible non-linear influences on European surface temperature. Clim Dyn 17:701–715. CrossRefGoogle Scholar
  85. Rajab JM, MatJafri MZ, Lim HS (2013) Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia. Atmos Environ 71:36–43. CrossRefGoogle Scholar
  86. Rajeevan M, Guhathakurta P, Thapliyal V (2000) New models for long range forecasts of summer monsoon rain fall over northwest and peninsular India. Meteorol Atmos Phys 73:211–225.
  87. Rao VB, Hada K (1990) Characteristics of rainfall over Brazil: annual variations and connections with the Southern Oscillation. Theor Appl Climatol 42:81–90CrossRefGoogle Scholar
  88. Ren R, Gu L, Fu H, Sun C (2017) Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery. J Appl Remote Sens 11(2):026014. CrossRefGoogle Scholar
  89. Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with the El Niño–Southern Oscillation. Mon Weather Rev 115:1606–1626CrossRefGoogle Scholar
  90. Ropelewski CF, Halpert MS (1989) Precipitation patterns associated with the high index phase of the Southern Oscillation. J Clim 2:268–284CrossRefGoogle Scholar
  91. Rusticucci M, Venegas S, Vargas W (2003) Warm and cold events in Argentina and their relationship with South Atlantic and South Pacific Sea Surface temperatures. J Geophys Res Oceans 108(C11):3356. CrossRefGoogle Scholar
  92. Rusticucci M, Kyselý J, Almeira G, Lhotka O (2016) Long-term variability of heat waves in Argentina and recurrence probability of the severe 2008 heat wave in Buenos Aires. Theor Appl Climatol 124:679–689. CrossRefGoogle Scholar
  93. Rusticucci M, Barrucand M, Collazo S (2017) Temperature extremes in the Argentina central region and their monthly relationship with the mean circulation and ENSO phases. Int J Climatol 37:3003–3017. CrossRefGoogle Scholar
  94. Rutllant JA (2004) Large-scale atmospheric circulation features associated with the 1997–1999 ENSO cycle and their consequences in the central-Chile precipitation regime. El Niño-La Niña 1997–2000. Their effects in Chile. CONA, Chile, Valparaíso. pp. 61–76.Google Scholar
  95. Rutllant JA, Aceituno P (1991) Southern Hemisphere circulation signals in connection with winter rainfall forecasting in central Chile. International Centre for Theoretical Physics, Trieste, Italy. Internal Report IC/91/64: 20 p.Google Scholar
  96. Saji NH, Yamagata T (2003) Possible impacts of Indian Ocean dipole mode events on global climate. Clim Res 25:151–169. CrossRefGoogle Scholar
  97. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401(23):360–363. CrossRefGoogle Scholar
  98. Saji NH, Ambrizzi T, Ferraz SET (2005) Indian Ocean dipole mode events and austral surface air temperature anomalies. Dyn Atmos Oceans 39:87–101. CrossRefGoogle Scholar
  99. Salio P, Nicolini M, Saulo AC (2002) Chaco low-level jet events characterization during the austral summer season. J Geophys Res 107(D24):4816. CrossRefGoogle Scholar
  100. Schwerdtfeger W (1976) Climates of Central and South America. World Surv. Climatol, vol 12. Elsevier Sci, New York, p 522Google Scholar
  101. Seluchi ME, Saulo AC, Nicolini M, Satyamurty P (2003) The northwestern Argentinean low: a study of two typical events. Mon Weather Rev 131:2361–2378.<2361:TNALAS>2.0.CO;2 CrossRefGoogle Scholar
  102. Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443(7108):205–209. CrossRefGoogle Scholar
  103. Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Teuling AJ (2010) Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci Rev 99(3):125–161.
  104. Sifuzzaman M, Islam M, Ali MZ (2009) Application of wavelet transform and its advantages compared to Fourier transform. J Phys Sci 13:121–134Google Scholar
  105. Silvestri GE, Vera CS (2003) Antarctic Oscillation signal on precipitation anomalies over southeastern South America. Geophys Res Lett 30(21):2115. CrossRefGoogle Scholar
  106. Slingo J, Palmer T (2011) Uncertainty in weather and climate prediction. Phil Trans R Soc A 369:4751–4767. CrossRefGoogle Scholar
  107. Solman S, Núñez M (1999) Local estimates of global climate change: a statistical downscaling approach. Int J Climatol 19:835–861CrossRefGoogle Scholar
  108. Srivastava A, Sinha Ray K (2000) Prediction of SST anomalies of east Pacific Ocean (Nino 3 region) using a statistical model. Theor Appl Climatol 66:131–138. CrossRefGoogle Scholar
  109. Statheropoulos M, Vassiliadis N, Pappa A (1998) Principal component and canonical correlation analysis for examining air pollution and meteorological data. Atmos Environ 32:1087–1095CrossRefGoogle Scholar
  110. Steinberg D. (2014) Why data scientists split data into train and test. Accessed 27 September 2017.
  111. Tong CHM, Yim SHL, Rothenberg D, Wang C, Lin CY, Chen YD, Lau NC (2018) Assessing the impacts of seasonal and vertical atmospheric conditions on air quality over the Pearl River Delta region. Atmos Environ 180:69–78. CrossRefGoogle Scholar
  112. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Amer Meteor Soc 79:61–78CrossRefGoogle Scholar
  113. Trenberth KE, Caron JM (2000) The Southern Oscillation revisited: sea level pressures, surface temperatures and precipitation. J Clim 13:4358–4365.<4358:TSORSL>2.0.CO;2 CrossRefGoogle Scholar
  114. Vargas W, Penalba O, Minetti J (1999) Monthly precipitation in areas of Argentina and the ENSO: a focus on decision problems. Meteorológica 24:3–22Google Scholar
  115. Vera C, Silvestri G, Barros V, Carril A (2004) Differences in El Niño response over the Southern Hemisphere. J Clim 17:1741–1753.<1741:DIENRO>2.0.CO;2 CrossRefGoogle Scholar
  116. Vincent LA, Peterson TC, Barros VR, Marino MB, Rusticucci M, Carrasco G, Ramirez E, Alves LM, Ambrizzi T, Berlato MA, Grimm AM, Marengo JA, Molion L, Moncunill DF, Rebello E, Anunciação YMT, Quintana J, Santos JL, Baez J, Coronel G, Garcia J, Trebejo I, Bidegain M, Haylock MR, Karoly D (2005) Observed trends in indices of daily temperature extremes in South America 1960–2000. J Clim 18:5011–5023. CrossRefGoogle Scholar
  117. von Storch H, Zorita E, Cubasch U (1993) Downscaling of climate change estimate to regional scales: application to winter rainfall on the Iberian Peninsula. J Clim 6:1161–1171CrossRefGoogle Scholar
  118. Wainer I, Prado LF, Khodri M, Otto-Bliesner B (2014) Reconstruction of the South Atlantic subtropical dipole index for the past 12000 years from surface temperature proxy. Sci Rep 4:5291. CrossRefGoogle Scholar
  119. Wei L, Feng Q, Deo RC (2018) Changes in climatic elements in the Pan-Hexi region during 1960–2014 and responses to global climatic changes. Theor Appl Climatol 133(1–2):405–420. CrossRefGoogle Scholar
  120. Whan K, Zscheischler J, Orth R, Shongwe M, Rahimi M, Asare EO, Seneviratne SI (2015) Impact of soil moisture on extreme maximum temperatures in Europe. Weather Clim Extremes 9:57–67. CrossRefGoogle Scholar
  121. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83CrossRefGoogle Scholar
  122. Yu ZP, Chu PS, Schroeder T (1997) Predictive skills of seasonal to annual rainfall variations in the U.S. affiliated Pacific Islands: canonical correlation analysis and multivariate principal component regression approaches. J Clim 10:2586–2599.;2 CrossRefGoogle Scholar
  123. Zamboni L, Mechoso CR, Kucharski F (2010) Relationships between upper-level circulation over South America and rainfall over southeastern South America: a physical base for seasonal predictions. J Clim 23(12):3300–3315. CrossRefGoogle Scholar
  124. Zar JH (1984) Biostatistical analysis, 2nd edn. Prentice-Hall, Inc., Englewood Cliffs, 718 pGoogle Scholar
  125. Zhang Y, Wallace J, Battisti D (1997) ENSO-like interdecadal variability: 1900–93. J Clim 10:1004–1020.<1004:ELIV>2.0.CO;2 CrossRefGoogle Scholar
  126. Zhang W, Lou IC, Kong Y, Ung WK, Mok M (2013) Eutrophication analysis and principal component regression for two subtropical storage reservoirs in Macau. Desalin Water Treat 51:7331–7340. CrossRefGoogle Scholar
  127. Zhou H, Liu Y (2017) Spatio-temporal pattern of meteorological droughts and its possible linkage with climate variability. Int J Climatol 38(4):2082–2096. CrossRefGoogle Scholar
  128. Zitto ME, Barrucand MG, Piotrkowski R, Canziani PO (2016) 110 years of temperature observations at Orcadas Antarctic Station: multidecadal variability. Int J Climatol 36(2):809–823. CrossRefGoogle Scholar

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

Authors and Affiliations

  1. 1.Department of Atmospheric and Ocean Sciences, Faculty of Exact and Natural SciencesUniversity of Buenos Aires (DCAO-FCEN-UBA)Buenos AiresArgentina
  2. 2.National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina

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