Publication: S^4CAST v2.0: sea surface temperature based statistical seasonal forecast model
Loading...
Official URL
Full text at PDC
Publication Date
2015
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Copernicus Publications
Citations
Exportar
Abstract
Sea surface temperature is the key variable when tackling seasonal to decadal climate forecasts. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. In addition, recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability and investigate possible teleconnections, the sea surface temperature based statistical seasonal foreCAST model (S^4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of the S^4CAST model whose operation is focused on studying the impacts of sea surface temperature on any climate-related variable. Two applications focused on analyzing the predictability of different climatic events have been implemented as benchmark examples.
Description
© Author(s) 2015. CC Attribution 3.0 License.
© Copernicus Publications on behalf of the European Geosciences Union.
The research leading to these results received funding from the PREFACE-EU project (EU FP7/2007-2013) under grant agreement no. 603521, Spanish national project MINECO (CGL2012-38923-C02-01) and the VR: 101/11 project from the VIII UCM Call for Cooperation and Development projects. We also appreciate the work done by SOURCEFORGE.NET® staff in creating NetCDF libraries for MATLAB®, and of course, thanks also to the reviewers, editors and their advice and/or criticism.
UCM subjects
Unesco subjects
Keywords
Citation
1. Adams, R. M., Chen, C. C., McCarl, B. A., and Weiher, R. F.: The economic consequences of ENSO events for agriculture, Clim. Res., 13, 165–172, 1999.
2. Ault, T. R., Cole, J. E., and St George, S.: The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models, Geophys. Res. Lett., 39, L21705, doi:10.1029/2012GL053424, 2012.
3. Baboo, S. S. and Shereef, I. K.: An efficient weather forecasting system using artificial neural network, International Journal of Environmental Science and Development, 1, 2010–0264, 2010. Barnett, T. P.: Monte Carlo climate forecasting, J. Climate, 8, 1005– 1022, 1995.
4. Barnett, T. P. and Preisendorfer, R.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis, Mon. Weather Rev., 115, 1825–1850, 1987.
5. Barnett, T. P., Graham, N., Pazan, S., White, W., Latif, M., and Flügel, M.: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean-atmosphere model, J. Climate, 6, 1545–1566, 1993.
6. Barnston, A. G.: Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score, Weather Forecast., 7, 699–709, 1992. Barnston, A. G. and Ropelewski, C. F.: Prediction of ENSO episodes using canonical correlation analysis, J. Climate, 5, 1316–1345, 1992.
7. Barnston, A. G. and Smith, T. M.: Specification and prediction of global surface temperature and precipitation from global SST using CCA, J. Climate, 9, 2660–2697, 1996.
8. Barnston, A. G. and Tippett, M. K.: Climate information, outlooks, and understanding – where does the IRI stand?, Earth Perspectives, 1, 1–17, 2014.
9. Barnston, A. G. and van den Dool, H. M.: A degeneracy in crossvalidated skill in regression-based forecasts, J. Climate, 6, 963– 977, 1993.
10. Barnston, A. G., van den Dool, H. M., Rodenhuis, D. R., Ropelewski, C. R., Kousky, V. E., O’Lenic, E. A., and Leetmaa, A.: Long-lead seasonal forecasts-Where do we stand?, B. Am. Meteorol. Soc., 75, 2097–2114, 1994.
11. Barnston, A. G., He, Y., and Glantz, M. H.: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset, B. Am. Meteorol. Soc., 80, 217–243, 1999.
12. Barnston, A. G., Tippet, M. K., van den Dool, H. M., and Unger, D. A.: Toward an Improved Multi-model ENSO Prediction, J. Appl. Meteorol. Clim., 54, 1579–1595, doi:10.1175/JAMC-D- 14-0188.1, 2015.
13. Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, doi:10.5194/essd-5-71-2013, 2013.
14. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, 2013.
15. Biasutti, M., Sobel, A. H., and Kushnir, Y.: AGCM precipitation biases in the tropical Atlantic, J. Climate, 19, 935–958, 2006.
16. Bjerknes, J.: Atmospheric teleconnections from the equatorial pacific 1, Mon. Weather Rev., 97, 163–172, 1969.
18. Bretherton, C. S., Smith, C., and Wallace, J. M.: An intercomparison of methods for finding coupled patterns in climate data, J. Climate, 5, 541–560, 1992.
19. Brown, J. N., Gupta, A. S., Brown, J. R., Muir, L. C., Risbey, J. S., Whetton, P., and Wijffels, S. E.: Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific, Climatic Change, 119, 147–161, 2013.
20. Bulić, I. H. and Kucharski, F.: Delayed ENSO impact on spring precipitation over North/Atlantic European region, Clim. Dynam., 382, 2593–2612, 2012.
21. Camberlin, P., Janicot, S., and Poccard, I.: Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO, Int. J. Climatol., 21, 973–1005, 2001.
22. Cane, M. A., Zebiak, S. E., and Dolan, S. C.: Experimental forecasts of EL Nino, Nature, 321, 827–832, 1986.
23. Chang, P., Fang, Y., Saravanan, R., Ji, L., and Seidel, H.: The cause of the fragile relationship between the Pacific El Nino and the Atlantic Nino, Nature, 443, 324–328, 2006.
24. Cherry, S.: Singular value decomposition analysis and canonical correlation analysis, J. Climate, 9, 2003–2009, 1996.
25. Cherry, S.: Some comments on singular value decomposition analysis, J. Climate, 10, 1759–1761, 1997.
26. Chung, C. E. and Ramanathan, V.: Weakening of North Indian SST gradients and the monsoon rainfall in India and the Sahel, J. Climate, 19, 2036–2045, 2006.
27. Coelho, C. A. S., Stephenson, D. B., Balmaseda, M., Doblas-Reyes, F. J., and van Oldenborgh, G. J.: Toward an integrated seasonal forecasting system for South America, J. Climate, 19, 3704– 3721, 2006.
28. Dayan, H., Vialard, J., Izumo, T., and Lengaigne, M.: Does sea surface temperature outside the tropical Pacific contribute to enhanced ENSO predictability?, Clim. Dynam., 43, 1311–1325, 2014.
29. Deng, X., Huang, J., Qiao, F., Naylor, R. L., Falcon, W. P., Burke, M., and Battisti, D.: Impacts of El Nino-Southern Oscillation events on China’s rice production, J. Geogr. Sci., 20, 3–16, 2010.
30. Diatta, S. and Fink, A. H.: Statistical relationship between remote climate indices andWest African monsoon variability, Int. J. Climatol., 34, 3348–3367, doi:10.1002/joc.3912, 2014.
31. Ding, H., Keenlyside, N. S., and Latif, M.: Impact of the equatorial Atlantic on the El Nino southern oscillation, Clim. Dynam., 38, 1965–1972, 2012.
32. Doi, T., Vecchi, G. A., Rosati, A. J., and Delworth, T. L.: Biases in the Atlantic ITCZ in seasonal-interannual variations for a coarseand a high-resolution coupled climate model, J. Climate, 25, 5494–5511, 2012.
33. Drosdowsky,W. and Chambers, L. E.: Near-global sea surface temperature anomalies as predictors of Australian seasonal rainfall, J. Climate, 14, 1677–1687, 2001.
34. Elsner, J. B. and Schmertmann, C. P.: Assessing forecast skill through cross validation, Weather Forecast., 9, 619–624, 1994. Enfield, D. B. and Cid-Serrano, L.: Projecting the risk of future climate shifts, Int. J. Climatol., 26, 885–895, 2006.
35. Folland, C. K., Palmer, T. N., and Parker, D. E.: Sahel rainfall and worldwide sea temperatures, 1901–85, Nature, 320, 602–607, 1986.
36. Fontaine, B. and Janicot, S.: Sea surface temperature fields associated with West African rainfall anomaly types, J. Climate, 9, 2935–2940, 1996.
37. Fontaine, B., Trzaska, S., and Janicot, S.: Evolution of the relationship between near global and Atlantic SST modes and the rainy season in West Africa: statistical analyses and sensitivity experiments, Clim. Dynam., 14, 353–368, 1998.
38. Fontaine, B., Philippon, N., and Camberlin, P.: An improvement of June–September rainfall forecasting in the Sahel based upon region April–May moist static energy content (1968–1997), Geophys. Res. Lett., 26, 2041–2044, 1999.
39. Fontaine, B., Monerie, P. A., Gaetani, M., and Roucou, P.: Climate adjustments over the African-Indian monsoon regions accompanying Mediterranean Sea thermal variability, J. Geophys. Res.- Atmos., 116, D23122, doi:10.1029/2011JD016273, 2011.
40. Frankignoul, C. and Hasselmann, K.: Stochastic climate models, part II application to sea-surface temperature anomalies and thermocline variability, Tellus, 29, 289–305, 1977.
41. Gaetani, M., Fontaine, B., Roucou, P., and Baldi, M.: Influence of the Mediterranean Sea on the West African monsoon: Intraseasonal variability in numerical simulations, J. Geophys. Res.- Atmos., 115, D24115, doi:10.1029/2010JD014436, 2010.
42. Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron)–a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, 1998.
43. Garric, G., Douville, H., and Déqué, M.: Prospects for improved seasonal predictions of monsoon precipitation over Sahel, Int. J. Climatol., 22, 331–345, 2002.
44. Giannini, A., Chiang, J. C., Cane, M. A., Kushnir, Y., and Seager, R.: The ENSO teleconnection to the tropical Atlantic Ocean: contributions of the remote and local SSTs to rainfall variability in the tropical Americas, J. Climate, 14, 4530–4544, 2001.
45. Giannini, A., Saravanan, R., and Chang, P.: Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales, Science, 302, 1027–1030, 2003.
46. Gill, A.: Some simple solutions for heat-induced tropical circulation, Q. J. Roy. Meteor. Soc., 106, 447–462, 1980.
47. Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203–1211, 1972.
48. Hansen, J. W., Hodges, A. W., and Jones, J. W.: ENSO Influences on Agriculture in the Southeastern United States, J. Climate, 11, 404–411, 1998.
49. Ham, Y. G., Kug, J. S., Park, J. Y., and Jin, F. F.: Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events, Nat. Geosci., 6, 112–116, 2013a.
50. Ham, Y. G., Sung, M. K., An, S. I., Schubert, S. D., and Kug, J. S.: Role of tropical Atlantic SST variability as a modulator of El Niño teleconnections, Asia-Pac. J. Atmos. Sci., 1–15, 2013b. Harrison, D. E. and Larkin, N. K.: El Niño-Southern Oscillation sea surface temperature and wind anomalies, 1946–1993, Rev. Geophys., 36, 353–399, 1998.
51. Hasselmann, K.: Stochastic climate models part I. Theory, Tellus, 28, 473–485, 1976.
52. Haylock, M. R., Peterson, T. C., Alves, L. M., Ambrizzi, T., Anunciação, Y. M. T., Baez, J., and Vincent, L. A.: Trends in total and extreme South American rainfall in 1960–2000 and links with sea surface temperature, J. Climate, 19, 1490–1512, 2006.
53. Hsieh, W. W.: Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach, J. Climate, 14, 2528–2539, 2001.
54. Hsieh,W.W. and Tang, B.: Applying neural network models to prediction and data analysis in meteorology and oceanography, B. Am. Meteorol. Soc., 79, 1855–1870, 1998.
55. Janicot, S.: Spatiotemporal variability of West African rainfall. Part I: Regionalizations and typings, J. Climate, 5, 489–497, 1992. Janicot, S., Moron, V., and Fontaine, B.: Sahel droughts and ENSO dynamics, Geophys. Res. Lett., 23, 515–518, 1996.
56. Janicot, S., Harzallah, A., Fontaine, B., and Moron, V.:West African monsoon dynamics and eastern equatorial Atlantic and Pacific SST anomalies (1970–88), J. Climate, 11, 1874–1882, 1998.
57. Janicot, S., Trzaska, S., and Poccard, I.: Summer Sahel-ENSO teleconnection and decadal time scale SST variations, Clim. Dynam., 18, 303–320, 2001.
58. Janowiak, J. E.: An investigation of interannual rainfall variability in Africa, J. Climate, 1, 240–255, 1988.
59. Ji, M., Kumar, A., and Leetmaa, A.: A multiseason climate forecast system at the National Meteorological Center, B. Am. Meteorol. Soc., 75, 569–577, 1994a.
60. Ji, M., Kumar, A., and Leetmaa, A.: An experimental coupled forecast system at the National Meteorological Center, Tellus A, 46, 398–418, 1994b.
61. Joly, M. and Voldoire, A.: Influence of ENSO on the West African monsoon: temporal aspects and atmospheric processes, J. Climate, 22, 3193–3210, 2009.
62. Keenlyside, N. S., Ding, H., and Latif, M.: Potential of equatorial Atlantic variability to enhance El Niño prediction, Geophys. Res. Lett., 40, 2278–2283, 2013.
63. Klein, S. A., Soden, B. J., and Lau, N. C.: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge, J. Climate, 12, 917–932, 1999.
64. Klein, W. H. and Glahn, H. R.: Forecasting local weather by means of model output statistics, B. Am. Meteorol. Soc., 55, 1217– 1227, 1974.
65. Knutti, R., Stocker, T. F., Joos, F., and Plattner, G. K.: Probabilistic climate change projections using neural networks, Clim. Dynam., 21, 257–272, 2003.
66. Korecha, D. and Barnston, A. G.: Predictability of June–September rainfall in Ethiopia, Mon. Weather Rev., 135, 628–650, 2007. Kovats, R. S.: El Niño and human health, B. World Health Organ., 78, 1127–1135, 2000.
67. Kovats, R. S., Bouma, M. J., Hajat, S., Worrall, E., and Haines, A.: El Niño and health, The Lancet, 362, 1481–1489, 2003.
68. Latif, M. and Barnett, T. P.: Interactions of the tropical oceans, J. Climate, 8, 952–964, 1995.
69. Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127, 1990.
70. Legler, D. M., Bryant, K. J., and O’Brien, J. J.: Impact of ENSOrelated climate anomalies on crop yields in the US, Climatic Change, 42, 351–375, 1999.
71. Li, G. and Xie, S. P.: Origins of tropical-wide SST biases in CMIP multi-model ensembles, Geophys. Res. Lett., 39, L22703, doi:10.1029/2012GL053777, 2012.
72. Li, G. and Xie, S. P.: Tropical Biases in CMIP5 Multimodel Ensemble: The Excessive Equatorial Pacific Cold Tongue and Double ITCZ Problems, J. Climate, 27, 1765–1780, 2014.
73. Li, Z. and Kafatos, M.: Interannual variability of vegetation in the United States and its relation to El Nino/Southern Oscillation, Remote Sens. Environ., 71, 239–247, 2000.
74. Lin, J. L.: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean-atmosphere feedback analysis, J. Climate, 20, 4497– 4525, 2007.
75. Linthicum, K. J., Anyamba, A., Chretien, J. P., Small, J., Tucker, C. J., and Britch, S. C.: The role of global climate patterns in the spatial and temporal distribution of vector-borne disease, in: Vector Biology, Ecology and Control, 3–13, Springer, the Netherlands, 2010.
76. Livezey, R. E. and Chen, W. Y.: Statistical field significance and its determination by Monte Carlo techniques, Mon. Weather Rev., 111, 46–59, 1983.
77. López-Parages, J. and Rodríguez-Fonseca, B.: Multidecadal modulation of El Niño influence on the Euro-Mediterranean rainfall, Geophys. Res. Lett., 39, L02704, doi:10.1029/2011GL050049, 2012.
78. López-Parages, J., Rodrígez-Fonseca, B., and Terray, L.: A mechanism for the multidecadal modulation of ENSO teleconnections with Europe, Clim. Dynam., 45, 867–880, 2014.
79. Losada, T., Rodríguez-Fonseca, B., Polo, I., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: Tropical response to the Atlantic Equatorial mode: AGCM multimodel approach, Clim. Dynam., 35, 45–52, 2010a.
80. Losada, T., Rodríguez-Fonseca, B., Janicot, S., Gervois, S., Chauvin, F., and Ruti, P.: A multi-model approach to the Atlantic Equatorial mode: impact on the West African monsoon, Clim. Dynam., 35, 29–43, 2010b.
81. Losada, T., Rodríguez-Fonseca, B., Mohino, E., Bader, J., Janicot, S., and Mechoso, C. R.: Tropical SST and Sahel rainfall: A non-stationary relationship, Geophys. Res. Lett., 39, L12705, doi:10.1029/2012GL052423, 2012.
82. Lu, J.: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought, Clim. Dynam., 33, 445–460, 2009.
83. Maia, A. H., Meinke, H., Lennox, S., and Stone, R.: Inferential, nonparametric statistics to assess the quality of probabilistic forecast systems, Mon. Weather Rev., 135, 351–362, 2007.
84. Majda, A. J., Timofeyev, I., and Eijnden, E. V.: Models for stochastic climate prediction, P. Natl. Acad. Sci., 96, 14687–14691, 1999.
85. Martín-Rey, M., Polo, I., Rodríguez-Fonseca, B., and Kucharski, F.: Changes in the interannual variability of the tropical Pacific as a response to an equatorial Atlantic forcing, Sci. Mar., 76, 105– 116, 2012.
86. Martín-Rey, M., Rodríguez-Fonseca, B., Polo, I., and Kucharski, F.: On the Atlantic–Pacific Niños connection: a multidecadal modulated mode, Clim. Dynam., 43, 3163–3178, 2014.
87. Martín-Rey, M., Rodríguez-Fonseca, B., and Polo, I.: Atlantic opportunities for ENSO prediction, Geophys. Res. Lett., 42, 6802– 6810, doi:10.1002/2015GL065062, 2015.
88. Mason, S. J., Goddard, L., Graham, N. E., Yulaeva, E., Sun, L., and Arkin, P. A.: The IRI seasonal climate prediction system and the 1997/98 El Niño event, B. Am. Meteorol. Soc., 80, 1853–1873, 1999.
89. McMichael, A. J., Woodruff, R. E., and Hales, S.: Climate change and human health: present and future risks, The Lancet, 367, 859–869, 2006.
90. Michaelsen, J.: Cross-validation in statistical climate forecast models, J. Clim. Appl. Meteorol., 26, 1589–1600, 1987.
91. Mohino, E., Janicot, S., and Bader, J.: Sahel rainfall and decadal to multi-decadal sea surface temperature variability, Clim. Dynam., 37, 419–440, 2011.
92. Mokhov, I. I. and Smirnov, D. A.: El Niño–Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices, Geophys. Res. Lett., 33, L03708,doi:10.1029/2005GL024557, 2006.
93. Naylor, R. L., Falcon, W. P., Rochberg, D., and Wada, N.: Using El Nino/Southern Oscillation climate data to predict rice production in Indonesia, Climatic Change, 50, 255–265, 2001.
94. Newman, M. and Sardeshmukh, P. D.: A caveat concerning singular value decomposition, J. Climate, 8, 352–360, 1995.
95. Nnamchi, H. C. and Li, J.: Influence of the South Atlantic Ocean dipole on West African summer precipitation, J. Climate, 24, 1184–1197, 2011.
96. Nnamchi, H. C., Li, J., and Anyadike, R. N.: Does a dipole mode really exist in the South Atlantic Ocean?, J. Geophys. Res.-Atmos., 116, 2011.
97. Palmer, T. N.: Influence of the Atlantic, Pacific and Indian oceans on Sahel rainfall, Nature, 322, 251–253, doi:10.1038/322251a0, 1986.
98. Patz, J. A.: A human disease indicator for the effects of recent global climate change, P. Natl. Acad. Sci., 99, 12506–12508, 2002.
99. Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A.: Impact of regional climate change on human health, Nature, 438, 310–317, 2005.
100. Penland, C. and Matrosova, L.: Prediction of tropical Atlantic sea surface temperatures using linear inverse modeling, J. Climate, 11, 483–496, 1998.
101. Penland, C. and Sardeshmukh, P. D.: The optimal growth of tropical sea surface temperature anomalies, J. Climate, 8, 1999–2024, 1995.
102. Phillips, J. G., Cane, M. A., and Rosenzweig, C.: ENSO, seasonal rainfall patterns and simulated maize yield variability in Zimbabwe, Agr. Forest Meteorol., 90, 39–50, 1998.
103. Podestá, G. P., Messina, C. D., Grondona, M. O., and Magrin, G. O.: Associations between grain crop yields in central-eastern Argentina and El Niño-Southern Oscillation, J. Appl. Meteorol., 38, 1488–1498, 1999.
104. Polo, I., Rodríguez-Fonseca, B., Losada, T., and García-Serrano, J.: Tropical Atlantic Variability modes (1979–2002). Part I: timeevolving SST modes related to West African rainfall, J. Climate, 21, 6457–6475, 2008.
105. Polo , I., Martin-Rey, M., Rodriguez-Fonseca, B., Kucharski, F., and Mechoso, C. R.: Processes in the Pacific La Niña onset triggered by the Atlantic Niño, Clim. Dynam., 44, 115–131, 2015.
106. Rasmusson, E. M. and Carpenter, T. H.: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño, Mon.Weather Rev., 110, 354–384, 1982.
107. Recalde-Coronel, G. C., Barnston, A. G., and Muñoz, Á. G.: Predictability of December-April Rainfall in Coastal and An- dean Ecuador, J. Appl. Meteorol. Clim., 53, 1471–1493, doi:10.1175/JAMC-D-13-0133.1, 2014.
108. Richter, I. and Xie, S. P.: On the origin of equatorial Atlantic biases in coupled general circulation models, Clim. Dynam., 31, 587– 598, 2008.
109. Richter, I., Xie, S. P., Wittenberg, A. T., and Masumoto, Y.: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation, Clim. Dynam., 38, 985–1001, 2012. Rimbu, N., Lohmann, G., Felis, T., and Pätzold, J.: Shift in ENSO teleconnections recorded by a northern Red Sea coral, J. Climate, 16, 1414–1422, 2003.
110. Rodríguez-Fonseca, B., Polo, I., García-Serrano, J., Losada, T., Mohino, E., Mechoso, C. R., and Kucharski, F.: Are Atlantic Niños enhancing Pacific ENSO events in recent decades?, Geophys. Res. Lett., 36, L20705, doi:10.1029/2009GL040048, 2009. Rodríguez-Fonseca, B., Janicot, S., Mohino, E., Losada, T., Bader, J., Caminade, C., and Voldoire, A.: Interannual and decadal SSTforced responses of theWest African monsoon, Atmos. Sci. Lett., 12, 67–74, 2011.
111. Rodríguez-Fonseca, B., Mohino, E., Mechoso, C. R., Caminade, C., Biasutti, M., Gaetani, M., García-Serrano, J., Vizy, E. K., Cook, K., Xue, Y., Polo, I., Losada, L., Druyan, L., Fontaine, B., Bader, J., Doblas-Reyes, F. J., Goddard, L., Janicot, S., Arribas, A., Lau, W., Colman, A., Vellinga, M., Rowell, D. P., Kucharski, F., and Voldoire, A.: Variability and Predictability of West African Droughts. A review on the role of Sea Surface Temperature Anomalies, J. Climate, 8, 4034–4060, doi:10.1175/JCLI-D-14-00130.1, 2015.
112. Roe, G. H. and Steig, E. J.: Characterization of millennial-scale climate variability, J. Climate, 17, 1929–1944, 2004.
113. Rowell, D. P.: Teleconnections between the tropical Pacific and the Sahel, Q. J. Roy. Meteor. Soc., 127, 1683–1706, 2001.
114. Rowell, D. P.: The impact of Mediterranean SSTs on the Sahelian rainfall season, J. Climate, 16, 849–862, 2003.
115. Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., and Ziese, M.: The new “GPCC Full Data Reanalysis Version 5” providing high-quality gridded monthly precipitation data for the global land-surface is public available since December 2010, GPCC status report December, 2010.
116. Saravanan, R. and Chang, P.: Interaction between tropical Atlantic variability and El Nino-southern oscillation, J. Climate, 13, 2177–2194, 2000.
117. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and Rudolf, B.: GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, 2014.
118. Schurer, A. P., Hegerl, G. C., Mann, M. E., Tett, S. F., and Phipps, S. J.: Separating forced from chaotic climate variability over the past millennium, J. Climate, 26, 6954–6973, 2013.
119. Shin, S. I., Sardeshmukh, P. D., and Webb, R. S.: Optimal tropical sea surface temperature forcing of North American drought, J. Climate, 23, 3907–3917, 2010.
120. Smith, T. M. and Reynolds, R. W.: Extended reconstruction of global sea surface temperatures based on COADS data (1854– 1997), J. Climate, 16, 1495–1510, 2003.
121. Smith, T. M. and Reynolds, R. W.: Improved extended reconstruction of SST (1854–1997), J. Climate, 17, 2466–2477, 2004.
122. Smith, T. M., Reynolds, R. W., Peterson, T. C., and Lawrimore, J.: Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006), J. Climate, 21, 2283–2296, 2008.
123. Shukla, R. P., Tripathi, K. C., Pandey, A. C., and Das, I. M. L.: Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach, Atmos. Res., 102, 99–109, 2011. Tang, B., Hsieh,W.W., Monahan, A. H., Tangang, F. T.: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures, J. Climate, 13, 287–293, 2000.
124. Tao, F., Yokozawa, M., Zhang, Z., Hayashi, Y., Grassl, H., and Fu, C.: Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation, Clim. Res., 28, 23–30, 2004.
125. Toniazzo, T. and Woolnough, S.: Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts, Clim. Dynam., 43, 2889–2913, 2013.
126. Travasso, M. I., Magrin, G. O., Grondona, M. O., and Rodríguez, G. R.: The use of SST and SOI anomalies as indicators of crop yield variability, Int. J Climatol., 29, 23–29, 2009.
127. Trenberth, K. E., Caron, J. M., Stepaniak, D. P., and Worley, S.: Evolution of El Niño–Southern Oscillation and global atmospheric surface temperatures, J. Geophys. Res.-Atmos., 107, AAC5.1–AAC5.17, doi:10.1029/2000JD000298, 2002.
128. Van den Dool, H. M.: Searching for analogues, how long must we wait?, Tellus A, 46, 314–324, 1994.
129. Van Oldenborgh, G. J. and Burgers, G.: Searching for decadal variations in ENSO precipitation teleconnections, Geophys. Res. Lett., 32, L15701, doi:10.1029/2005GL023110, 2005.
130. Vannière, B., Guilyardi, E., Madec, G., Doblas-Reyes, F. J., and Woolnough, S.: Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO, Clim. Dynam., 40, 963–981, 2013.
131. Verdin, J., Funk, C., Klaver, R., and Roberts, D.: Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: results for the 1998 maize growing season, Int. J. Remote Sens., 20, 2117–2124, 1999.
132. Vimont, D. J.: Analysis of the Atlantic meridional mode using linear inverse modeling: Seasonality and regional influences, J. Climate, 25, 1194–1212, 2012.
133. Vislocky, R. L. and Fritsch, J. M.: Improved model output statistics forecasts through model consensus, B. Am. Meteorol. Soc., 76, 1157–1164, 1995.
134. Wahl, S., Latif, M., Park, W., and Keenlyside, N.: On the tropical Atlantic SST warm bias in the Kiel Climate Model, Clim. Dynam., 36, 891–906, 2011.
135. Wallace, J. M., Smith, C., and Bretherton, C. S.: Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies, J. Climate, 5, 561–576, 1992.
136. Wang, S. Y., L’Heureux, M., and Chia, H. H.: ENSO prediction one year in advance using western North Pacific sea surface temperatures, Geophys. Res. Lett., 39, L05702, doi:10.1029/2012GL050909, 2012.
137. Ward, M. N.: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales, J. Climate, 11, 3167–3191, 1998.
138. Widmann, M.: One-dimensional CCA and SVD, and their relationship to regression maps, J. Climate, 18, 2785–2792, 2005.
139. Xue, Y., Chen, M., Kumar, A., Hu, Z. Z., and Wang, W.: Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP Climate Forecast System version 2, J. Climate, 26, 5358–5378, 2013.
140. Zebiak, S. E. and Cane, M. A.: A Model El Niño-Southern Oscillation, Mon. Weather Rev., 115, 2262–2278, 1987.