Advertisement

Climate Dynamics

, Volume 44, Issue 11–12, pp 2989–3014 | Cite as

Evaluation of observed and simulated teleconnections over the Euro-Atlantic region on the basis of partial least squares regression

  • N. Gonzalez-Reviriego
  • C. Rodriguez-Puebla
  • B. Rodriguez-Fonseca
Article

Abstract

An interesting topic in climate research is to determine the variability of teleconnection patterns under warming conditions. The North Atlantic Oscillation (NAO), the East Atlantic, the East Atlantic-West Russian and the Scandinavian (SCAND) patterns are the most important teleconnection patterns affecting Europe. Results associated with traditional methodologies for capturing these patterns, such as conventional and rotated empirical orthogonal function analysis, are difficult to intercompare when using different datasets. Therefore, we employed the method of partial least squares (PLS) regression to find a plausible representation of the teleconnections using the standard set of teleconnection patterns defined by National Oceanic and Atmospheric Administration’s Climate Prediction Center as a reference. The variability and trend of the teleconnection indices and patterns over the twentieth and twenty-first centuries were investigated for 20C3M and SRES A1B experiments from the third phase of the Coupled Model Intercomparison Project (CMIP) and compared with the twentieth-century reanalysis data. The results of this study show a positive trend for the NAO and a negative trend for the SCAND under a future climate scenario. With this study, we were able to extract consistent teleconnection patterns across different models, demonstrating the usefulness of the PLS regression in evaluating models and establishing the basis for future work using the fifth phase of CMIP data to assess atmospheric circulation trends and causes of regional climate change.

Keywords

Climate models Teleconnection patterns Euro-Atlantic region Partial least squares 

Notes

Acknowledgments

The authors are grateful to the two anonymous reviewers of this paper for their numerous and useful comments on the original manuscript. We are very grateful to Dr. Timothy Osborn, from the Climate Research Unit (CRU) of the University of East Anglia, for his helpful and productive conversations about some aspects of the paper. We would like to acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their role in making available the WCRP CMIP3 multi-model dataset. Support for this dataset is provided by the Office of Science, U. S. Department of Energy. We would like to thank the NCEP/NCAR for providing the reanalysis, the Climate Prediction Center (CPC) of NOAA for the Northern Hemisphere Teleconnection Indices and the developers of CDAT software. We also thank Guillaume Maze (http://www.guillaumemaze.org) for providing a MATLAB script for plotting good Taylor diagrams. We also thank Javier Vegas-Regidor for his support with programming. This work is supported under grants from the Spanish Ministry of Science and Innovation CGL2008-04619 and CGL2011-23209, from the Regional Government of Castile and Leon SA222/A11-2 with European FEDER funds and from the Spanish Ministry of Environment MOVAC ref.200800050084028.

Supplementary material

382_2014_2367_MOESM1_ESM.pdf (8 mb)
Supplementary material 1 (pdf 8222 KB)

References

  1. Abdi H (2010) Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip Rev Comput Stat 2(1):97–106CrossRefGoogle Scholar
  2. Barnes EA, Polvani L (2013) Response of the midlatitude jets, and of their variability, to increased greenhouse gases in the CMIP5 models. J Clim 26(18):7117–7135CrossRefGoogle Scholar
  3. Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115(6):1083–1126CrossRefGoogle Scholar
  4. Bladè I, Fortuny D, van Oldenborgh GJ, Liebmann B (2012) The summer North Atlantic Oscillation in CMIP3 models and related uncertainties in projected summer drying in Europe. J Geophys Res Atmos 117(D16):D16104. doi: 10.1029/2012JD017816 CrossRefGoogle Scholar
  5. Casado MJ, Pastor MA (2012) Use of variability modes to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn 38(1–2):225–237CrossRefGoogle Scholar
  6. Cattiaux J, Cassou C (2013) Opposite CMIP3/CMIP5 trends in the wintertime Northern Annular Mode explained by combined local sea ice and remote tropical influences. Geophys Res Lett 40(14):3682–3687CrossRefGoogle Scholar
  7. Chronis T, Raitsos DE, Kassis D, Sarantopoulos A (2011) The summer North Atlantic oscillation influence on the Eastern Mediterranean. J Clim 24(21):5584–5596CrossRefGoogle Scholar
  8. Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006) The community climate system model version 3 (CCSM3). J Clim 19(11):2122–2143CrossRefGoogle Scholar
  9. Comas-Bru L, McDermott F (2014) Impacts of the EA and SCA patterns on the European twentieth century NAO-winter climate relationship. Quart J R Meteorol Soc 140(679):354–363CrossRefGoogle Scholar
  10. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Bronnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY, Jones PD, Kruk MC, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli O, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Quart J R Meteorol Soc 137(654):1–28CrossRefGoogle Scholar
  11. Corti S, Molteni F, Palmer TN (1999) Signature of recent climate change in frequencies of natural atmospheric circulation regimes. Nature 398(6730):799–802CrossRefGoogle Scholar
  12. de Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst 18(3):251–263CrossRefGoogle Scholar
  13. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R, Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR, Lee HC, Lin SJ, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD, Shevliakova E, Sirutis JJ, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang R (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19(5):643–674CrossRefGoogle Scholar
  14. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3–4):527–546CrossRefGoogle Scholar
  15. Deser C, Phillips AS, Alexander MA, Smoliak BV (2014) Projecting North American climate over the next 50 years: uncertainty due to internal variability. J Clim 27(6):2271–2296CrossRefGoogle Scholar
  16. Diansky NA, Volodin EM (2002) Simulation of present-day climate with a coupled atmosphere-ocean general circulation model. Izvestiya Atmos Ocean Phys 38(6):732–747Google Scholar
  17. Dong B, Sutton RT, Woollings T (2011) Changes of interannual NAO variability in response to greenhouse gases forcing. Clim Dyn 37(7–8):1621–1641CrossRefGoogle Scholar
  18. Errasti I, Ezcurra A, Saenz J, Ibarra-Berastegi G (2011) Validation of IPCC AR4 models over the Iberian Peninsula. Theor Appl Climatol 103(1–2):61–79CrossRefGoogle Scholar
  19. Faller AJ (1981) An average correlation coefficient. J Appl Meteorol 20(2):203–205CrossRefGoogle Scholar
  20. Flato GM, Boer GJ, Lee WG, McFarlane NA, Ramsden D, Reader MC, Weaver AJ (2000) The Canadian Centre for climate modelling and analysis global coupled model and its climate. Clim Dyn 16(6):451–467CrossRefGoogle Scholar
  21. Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamsto NG, Sorteberg A (2003) Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21(1):27–51CrossRefGoogle Scholar
  22. Fyfe JC, Boer GJ, Flato GM (1999) The Arctic and Antarctic oscillations and their projected changes under global warming. Geophys Res Lett 26(11):1601–1604CrossRefGoogle Scholar
  23. Garthwaite PH (1994) An interpretation of partial least-squares. J Am Stat Assoc 89(425):122–127CrossRefGoogle Scholar
  24. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17CrossRefGoogle Scholar
  25. Gillett NA, Fyfe JC (2013) Annular mode changes in the CMIP5 simulations. Geophys Res Lett 40(6):1189–1193CrossRefGoogle Scholar
  26. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16(2–3):147–168CrossRefGoogle Scholar
  27. Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate system model. CSIRO Atmospheric Research Technical Paper No. 60:130Google Scholar
  28. Handorf D, Dethloff K (2012) How well do state-of-the-art atmosphere–ocean general circulation models reproduce atmospheric teleconnection patterns? Tellus A Dyn Meteorol Oceanogr 64:1–27. doi: 10.3402/tellusa.v64i0.19777
  29. Hannachi A, Jolliffe IT, Stephenson DB (2007) Empirical orthogonal functions and related techniques in atmospheric science: a review. Int J Climatol 27(9):1119–1152CrossRefGoogle Scholar
  30. Hannachi A, Unkel S, Trendafilov NT, Jolliffe IT (2009) Independent component analysis of climate data: a new look at EOF rotation. J Clim 22(11):2797–2812CrossRefGoogle Scholar
  31. Hannachi A, Barnes EA, Woollings T (2013) Behaviour of the winter North Atlantic eddy-driven jet stream in the CMIP3 integrations. Clim Dyn 41(3–4):995–1007CrossRefGoogle Scholar
  32. Hasumi H, Emori S (2004) K-1 coupled GCM (MIROC) description. Center for Climate System Research (CCSR), University of Tokyo, Technical reportsGoogle Scholar
  33. Hurrell JW (1995) Decadal trends in the North Atlantic oscillation: regional temperatures and precipitation. Science 269(5224):676–679CrossRefGoogle Scholar
  34. Hurrell JW, Kushnir Y, Visbeck M (2001) The North Atlantic oscillation. Science 291(5504):603–605CrossRefGoogle Scholar
  35. Hurrell JW, Deser C (2010) North Atlantic climate variability: the role of the North Atlantic Oscillation. J Mar Syst 79(3):231–244CrossRefGoogle Scholar
  36. 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 TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds)] Cambridge University Press, Cambridge, 1535 ppGoogle Scholar
  37. Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong BW, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The New Hadley Centre climate model (HadGEM1): evaluation of coupled simulations. J Clim 19(7):1327–1353CrossRefGoogle Scholar
  38. Jungclaus JH, Keenlyside N, Botzet M, Haak H, Luo JJ, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J Clim 19(16):3952–3972CrossRefGoogle Scholar
  39. Kalela-Brundin M (1999) Climatic information from tree-rings of Pinus sylvestris L. and a reconstruction of summer temperatures back to AD 1500 in Femundsmarka, eastern Norway, using partial least squares regression (PLS) analysis. Holocene 9(1):59–77Google Scholar
  40. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J Clim 23(10):2739–2758CrossRefGoogle Scholar
  41. Kuzmina SI, Bengtsson L, Johannessen OM, Drange H, Bobylev LP, Miles MW (2005) The North Atlantic Oscillation and greenhouse-gas forcing. Geophys Res Lett 32:L04703. doi: 10.1029/2004GL021064 Google Scholar
  42. Le Cozannet G, Lecacheux S, Delvallee E, Desramaut N, Oliveros C, Pedreros R (2011) Teleconnection pattern influence on sea-wave climate in the Bay of Biscay. J Clim 24(3):641–652CrossRefGoogle Scholar
  43. Lindgren F, Geladi P, Wold S (1993) The kernel algorithm for PLS. J Chemom 7(1):45–59CrossRefGoogle Scholar
  44. Lopez-Moreno JI, Vicente-Serrano SM, Moran-Tejeda E, Lorenzo-Lacruz J, Kenawy A, Beniston M (2011) Effects of the North Atlantic Oscillation (NAO) on combined temperature and precipitation winter modes in the Mediterranean mountains: Observed relationships and projections for the 21st century. Global Planet Change 77(1–2):62–76CrossRefGoogle Scholar
  45. Lorber A, Kowalski BR (1988) A note on the use of the partial least-squares method for multivariate calibration. Appl Spectrosc 42(8):1572–1574CrossRefGoogle Scholar
  46. Lucarini V, Russell GL (2002) Comparison of mean climate trends in the Northern Hemisphere between National Centers for Environmental Prediction and two atmosphere-ocean model forced runs. J Geophys Res Atmos 107(D15). doi: 10.1029/2001JD001247
  47. Luo DH, Gong TT, Diao Y, Zhou W (2007) Storm tracks and annular modes. Geophys Res Lett 34:L17701. doi: 10.1029/2007GL030436 CrossRefGoogle Scholar
  48. Mariotti A, Dell’Aquila A (2012) Decadal climate variability in the Mediterranean region: roles of large-scale forcings and regional processes. Clim Dyn 38(5–6):1129–1145CrossRefGoogle Scholar
  49. Marti O, Braconnot P, Bellier J, Benshila R, Bony S, Brockmann P, Cadule P, Caubel A, Denvil S, Dufresne JL, Fairhead L, Filiberti MA, Foujols MA, Fichefet TT, Friedlingstein P, Gosse H, Grandpeix JY, Hourdin FF, Krinner G, Lvi C, Madec G, Musat I, de Noblet N, Polcher J, Talandier C (2006) The new IPSL climate system model: IPSL-CM4. Note Ple Modlisation IPSL 26:1–86Google Scholar
  50. McHugh MJ, Rogers JC (2005) Multi-model representation of the North Atlantic Oscillation in the 20th and 21st centuries. Geophys Res Lett 32:L21713. doi: 10.1029/2005GL023679 CrossRefGoogle Scholar
  51. McIntosh AR, Lobaugh NJ (2004) Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23:S250–S263CrossRefGoogle Scholar
  52. McIntosh PC, Ash AJ, Smith MS (2005) From oceans to farms: the value of a novel statistical climate forecast for agricultural management. J Clim 18(20):4287–4302Google Scholar
  53. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88(9):1383–1394CrossRefGoogle Scholar
  54. Miller RL, Schmidt GA, Shindell DT (2006) Forced annular variations in the 20th century intergovernmental panel on climate change fourth assessment report models. J Geophys Res 111:D18101. doi: 10.1029/2005JD006323 CrossRefGoogle Scholar
  55. Molteni F, King MP, Kucharski F, Straus DM (2011) Planetary-scale variability in the northern winter and the impact of land-sea thermal contrast. Clim Dyn 37(1–2):151–170CrossRefGoogle Scholar
  56. Moore GWK, Renfrew IA, Pickart RS (2013) Multidecadal mobility of the North Atlantic oscillation. J Clim 26(8):2453–2466CrossRefGoogle Scholar
  57. Naes T, Martens H (1985) Comparison of prediction methods for multicollinear data. Commun Stat Simul Comput 14(3):545–576CrossRefGoogle Scholar
  58. Nieto S, Frias MD, Rodriguez-Puebla C (2004) Assessing two different climatic models and the NCEP-NCAR reanalysis data for the description of winter precipitation in the Iberian Peninsula. Int J Climatol 24(3):361–376CrossRefGoogle Scholar
  59. Nieto S, Rodriguez-Puebla C (2006) Comparison of precipitation from observed data and general circulation models over the Iberian Peninsula. J Clim 19(17):4254–4275CrossRefGoogle Scholar
  60. OrtizBevia MJ, Alvarez-Garcia FJ, Liguori G, Carretero JH (2012) The Western Mediterranean summer variability and its feedbacks. Clim Dyn 39(12):3103–3120CrossRefGoogle Scholar
  61. Osborn TJ, Briffa KR, Tett SFB, Jones PD, Trigo RM (1999) Evaluation of the North Atlantic oscillation as simulated by a coupled climate model. Clim Dyn 15(9):685–702CrossRefGoogle Scholar
  62. Osborn TJ (2004) Simulating the winter North Atlantic oscillation: the roles of internal variability and greenhouse gas forcing. Clim Dyn 22(6–7):605–623Google Scholar
  63. Osborn T (2011) Variability and changes in the North Atlantic oscillation index. In: Vicente-Serrano SM, Trigo RM (eds) Hydrological, socioeconomic and ecological impacts of the North Atlantic oscillation in the Mediterranean Region, Advances in Global Change Research, vol 46. Springer Science+Business Media B. V., Newyork, pp 9–22Google Scholar
  64. Panagiotopoulos F, Shahgedanova M, Stephenson DB (2002) A review of Northern Hemisphere winter-time teleconnection patterns. J Phys IV (Proceedings) 12(10):27–47CrossRefGoogle Scholar
  65. Pinto JG, Zacharias S, Fink AH, Leckebusch GC, Ulbrich U (2009) Factors contributing to the development of extreme North Atlantic cyclones and their relationship with the NAO. Clim Dyn 32(5):711–737CrossRefGoogle Scholar
  66. Preisendorfer RW (1988) Principal component analysis in meteorology and oceanography. Elsevier, New YorkGoogle Scholar
  67. Quadrelli R, Wallace JM (2004) A simplified linear framework for interpreting patterns of Northern Hemisphere wintertime climate variability. J Clim 17(19):3728–3744CrossRefGoogle Scholar
  68. Richman MB (1986) Rotation of principal components. J Climatol 6(3):293–335CrossRefGoogle Scholar
  69. Rodriguez-Fonseca B, Rodriguez-Puebla C (2010) Climate teleconnections affecting Iberian Peninsula. Climate variability. Predictability and expected changes. In: Perez FF, Boscolo R (eds) Report climate in Spain: past, present and future, CLIVAR-Spain, chap.4, pp 53–67Google Scholar
  70. Rodriguez-Fonseca B, Sanchez E, Arribas A (2005) Winter climate variability changes over Europe and the Mediterranean region under increased greenhouse conditions. Geophys Res Lett 32:L13702. doi: 10.1029/2005GL022800 CrossRefGoogle Scholar
  71. Rodriguez-Puebla C, Encinas AH, Nieto S, Garmendia J (1998) Spatial and temporal patterns of annual precipitation variability over the Iberian Peninsula. Int J Climatol 18(3):299–316CrossRefGoogle Scholar
  72. Rodriguez-Puebla C, Nieto S (2010) Trends of precipitation over the Iberian Peninsula and the North Atlantic oscillation under climate change conditions. Int J Climatol 30(12):1807–1815Google Scholar
  73. Rosipal R, Kramer N (2006) Overview and recent advances in partial least squares. Subspace, latent structure and feature selection 3940:34–51CrossRefGoogle Scholar
  74. Salas-Melia D, Chauvin F, Deque M, Douville H, Gueremy JF, Marquet P, Planton S, Royer JF, Tyteca S (2005) Description and validation of the CNRM-CM3 global coupled model. CNRM working note 103:36Google Scholar
  75. Schenk F, Wagner S, Zorita E (2009) Nonstationarity between the North Atlantic Oscillation (NAO) and its climate impact on Northern Europe. In AGU Fall meeting abstracts, vol 1, p 0261Google Scholar
  76. Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Bauer S, Cairns B, Canuto V, Cheng Y, Del Genio A, Faluvegi G, Friend AD, Hall TM, Hu YY, Kelley M, Kiang NY, Koch D, Lacis AA, Lerner J, Lo KK, Miller RL, Nazarenko L, Oinas V, Perlwitz J, Rind D, Romanou A, Russell GL, Sato M, Shindell DT, Stone PH, Sun S, Tausnev N, Thresher D, Yao MS (2006) Present-day atmospheric simulations using GISS ModelE: comparison to in situ, satellite, and reanalysis data. J Clim 19(2):153–192CrossRefGoogle Scholar
  77. Scoccimarro E, Gualdi S, Fogli PG, Manzini E, Grezio A, Navarra A (2007). INGV-SXG: a coupled atmosphere ocean sea-ice general circulation climate model. CMCC Research Paper, 15Google Scholar
  78. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63(324):1379–1389CrossRefGoogle Scholar
  79. Smoliak BV, Wallace JM, Stoelinga MT, Mitchell TP (2010) Application of partial least squares regression to the diagnosis of year-to-year variations in Pacific Northwest snowpack and Atlantic hurricanes. Geophys Res Lett 37:L03801. doi: 10.1029/2009GL041478 Google Scholar
  80. Stephenson DB, Pavan V, Collins M, Junge MM, Quadrelli R, Participating CMG (2006) North Atlantic oscillation response to transient greenhouse gas forcing and the impact on European winter climate: a CMIP2 multi-model assessment. Clim Dyn 27(4):401–420CrossRefGoogle Scholar
  81. Stoner AMK, Hayhoe K, Wuebbles DJ (2009) Assessing general circulation model simulations of atmospheric teleconnection patterns. J Clim 22(16):4348–4372CrossRefGoogle Scholar
  82. Tan YX, Shi LB, Tong WD, Hwang GTG, Wang C (2004) Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Comput Biol Chem 28(3):235–244CrossRefGoogle Scholar
  83. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192CrossRefGoogle Scholar
  84. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc Math Phys Eng Sci 365(1857):2053–2075CrossRefGoogle Scholar
  85. Thompson DWJ, Wallace JM (2001) Regional climate impacts of the northern hemisphere annular mode. Science 293(5527):85–89CrossRefGoogle Scholar
  86. Tobias RD (1995) An introduction to partial least squares regression. In: Proceedings of annual SAS users group international conference, 20th, Orlando, FLGoogle Scholar
  87. Trenberth KE, Branstator GW, Karoly D, Kumar A, Lau NC, Ropelewski C (1998) Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J Geophys Res Oceans 103(C7):14291–14324CrossRefGoogle Scholar
  88. Ulbrich U, Christoph M (1999) A shift of the NAO and increasing storm track activity over Europe due to anthropogenic greenhouse gas forcing. Clim Dyn 15(7):551–559CrossRefGoogle Scholar
  89. Vicente-Serrano SM, Lopez-Moreno JI (2008) Nonstationary influence of the North Atlantic Oscillation on European precipitation. J Geophys Res Atmos 113:D20120. doi: 10.1029/2008JD010382 CrossRefGoogle Scholar
  90. Villarini G, Smith JA, Vitolo R, Stephenson DB (2013) On the temporal clustering of US floods and its relationship to climate teleconnection patterns. Int J Climatol 33(3):629–640CrossRefGoogle Scholar
  91. Von Storch H (1995) Spatial patterns: EOFs and CCA. In: von Storch H, Navarra A (eds) Anal Clim Var. Springer-Verlag, India, pp 227–258CrossRefGoogle Scholar
  92. Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon Weather Rev 109(4):784–812CrossRefGoogle Scholar
  93. Wallace JM, Fu Q, Smoliak BV, Lin P, Johanson CM (2012) Simulated versus observe patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc Natl Acad Sci 109(36):14337–14342CrossRefGoogle Scholar
  94. Wang Y-H, Magnusdottir G (2012) The shift of the northern node of the NAO and cyclonic rossby wave breaking. J Clim 25(22):7973–7982CrossRefGoogle Scholar
  95. Washington WM, Weatherly JW, Meehl GA, Semtner AJ, Bettge TW, Craig AP, Strand WG, Arblaster J, Wayland VB, James R, Zhang Y (2000) Parallel climate model (PCM) control and transient simulations. Clim Dyn 16(10–11):755–774CrossRefGoogle Scholar
  96. Wold H (1966) Estimation of principal components and related models by iterative least squares. In: Krishnaiah PJ (ed) Multivar Anal. Academic Press, New York, pp 391–420Google Scholar
  97. Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear-regression. The partial least squares (PLS) approach to generalized inverses. Siam J Sci Stat Comput 5(3):735–743CrossRefGoogle Scholar
  98. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130CrossRefGoogle Scholar
  99. Woollings T, Hannachi A, Hoskins B, Turner A (2010) A regime view of the North Atlantic oscillation and its response to anthropogenic forcing. J Clim 23(6):1291–1307CrossRefGoogle Scholar
  100. Wyatt MG, Kravtsov S, Tsonis AA (2012) Atlantic multidecadal oscillation and northern hemisphere’s climate variability. Clim Dyn 38:929–949CrossRefGoogle Scholar
  101. Yongquiang Y, Xuehong Z, Yufu G (2004) Global coupled ocean–atmosphere general circulation models in LAGS/IAP. Adv Atmos Sci 21(3):444–455CrossRefGoogle Scholar
  102. Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resourc Manag 18(3):201–218CrossRefGoogle Scholar
  103. Yukimoto S, Noda A, Kitoh A, Hosaka M, Yoshimura H, Uchiyama T, Shibata K, Arakawa O, Kusunoki S (2006) Present-day climate and climate sensitivity in the Meteorological Research Institute coupled GCM version 2.3 (MRI-CGCM2.3). J Meteorol Soc Jpn 84(2):333–363CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • N. Gonzalez-Reviriego
    • 1
  • C. Rodriguez-Puebla
    • 1
  • B. Rodriguez-Fonseca
    • 2
    • 3
  1. 1.Department of Atmospheric PhysicsUniversity of SalamancaSalamancaSpain
  2. 2.Department of Geophysics and MeteorologyComplutense University of MadridMadridSpain
  3. 3.Geosciences Institute (UCM-CSIC)MadridSpain

Personalised recommendations