Southern African summer-rainfall variability, and its teleconnections, on interannual to interdecadal timescales in CMIP5 models

  • Bastien DieppoisEmail author
  • Benjamin Pohl
  • Julien Crétat
  • Jonathan Eden
  • Moussa Sidibe
  • Mark New
  • Mathieu Rouault
  • Damian Lawler


This study provides the first assessment of CMIP5 model performances in simulating southern Africa (SA) rainfall variability in austral summer (Nov–Feb), and its teleconnections with large-scale climate variability at different timescales. Observed SA rainfall varies at three major timescales: interannual (2–8 years), quasi-decadal (8–13 years; QDV) and interdecadal (15–28 years; IDV). These rainfall fluctuations are, respectively, associated with El Niño Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO), interacting with climate anomalies in the South Atlantic and South Indian Ocean. CMIP5 models produce their own variability, but perform better in simulating interannual rainfall variability, while QDV and IDV are largely underestimated. These limitations can be partly explained by spatial shifts in core regions of SA rainfall variability in the models. Most models reproduce the impact of La Niña on rainfall at the interannual scale in SA, in spite of limitations in the representation of ENSO. Realistic links between negative IPO are found in some models at the QDV scale, but very poor performances are found at the IDV scale. Strong limitations, i.e. loss or reversal of these teleconnections, are also noted in some simulations. Such model errors, however, do not systematically impact the skill of simulated rainfall variability. This is because biased SST variability in the South Atlantic and South Indian Oceans strongly impact model skills by modulating the impact of Pacific modes of variability. Using probabilistic multi-scale clustering, model uncertainties in SST variability are primarily driven by differences from one model to another, or comparable models (sharing similar physics), at the global scale. At the regional scale, i.e. SA rainfall variability and associated teleconnections, while differences in model physics remain a large source of uncertainty, the contribution of internal climate variability is increasing. This is particularly true at the QDV and IDV scales, where the individual simulations from the same model tend to differentiate, and the sampling error increase.


Southern African rainfall variability Interannual to interdecadal timescales Sea-surface temperature anomalies Teleconnections CMIP5 models 



ERSST.v4, 20CR.v2, GPCC.v7 and COBE SST2 data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at The CRU TS 3.24.1 rainfall field were available from the Centre for Environmental Data Archival (CEDA) at The authors would like to thank Noel Keenlyside, Thomas Toniazzo and Yushi Morioka for their helpful discussions.

Supplementary material

382_2019_4720_MOESM1_ESM.eps (169.4 mb)
Fig. A1 Comparison between SST composite anomalies associated with wet and dry conditions in SA. ac Scatterplot of observed SST composite anomalies during wet (positive SRI anomalies = +1 SD) and dry (negative SRI anomalies = − 1 SD) conditions at the interannual (left), QDV (middle) and IDV (right) timescales. df as ac but for 95 historical runs from 28 CMIP5 models. Scatter plots are smoothed and coloured (low to high probability = blue, yellow to red) using a 2D kernel density estimate. Red and black lines refer to the regression lines between wet and dry SST composite anomalies, and associated correlation coefficients are provided in the lower left corner on each panel. (EPS 173447 KB)
382_2019_4720_MOESM2_ESM.eps (1.9 mb)
Fig. A2 Step-by-step process of the multi-scale bootstrap clustering. Step-0: 10 simulated patterns of global SST variability are submitted to the clustering approach. Step-1: simulated patterns are resampled ni times, using rj scales (referring to different sizes of the spatial domain). Step-2: ni × rj Agglomerative hierarchical clustering are produced, using Ward’s agglomerative criteria applied to Euclidian distances. Step-3: the probability of each simulations to be clustered with the others (red values) is estimated. Here, only two clusters are significantly robust at p ≥ 0.90. (EPS 1898 KB)
382_2019_4720_MOESM3_ESM.eps (64 kb)
Fig. A3 Percentage of occurrence of significant signals within the interannual, QDV and IDV timescales of SRI variability in all (95) model simulations, in all (28) models and all (16) institutions using the CMIP5 historical experiments. Statistical significance was estimated at p = 0.05 based 1000 Monte Carlo simulations of the red noise background spectrum. (EPS 64 KB)
382_2019_4720_MOESM4_ESM.eps (43 kb)
Fig. A4 Boxplots of the correlations between simulations of the same models (intra-ensembles; blue), and of different models (inter-models; red), of the CMIP5 historical experiments in reproducing spatial patterns of SA rainfall variability at the three different timescales. (EPS 42 KB)
382_2019_4720_MOESM5_ESM.eps (41.7 mb)
Fig. A5 Distributions of model simulations in clustering patterns of SA rainfall variability, global SST variability, and teleconnections at the interannual to interdecadal timescales. The six selected clusters, which are shown in Figs. 5, 7 and 8, are in bold. (EPS 42663 KB)
382_2019_4720_MOESM6_ESM.eps (48 kb)
Fig. A6 Boxplots of the correlations between simulations of the same models (intra-ensembles; blue), and of different models (inter-models; red), of the CMIP5 historical experiments in reproducing spatial patterns of global SST variability (shaded), and SST composite anomalies associated with SA rainfall variability (not shaded) at the three different timescales. (EPS 48 KB)
382_2019_4720_MOESM7_ESM.eps (154 kb)
Fig. A7 Mean annual cycle of monthly standard deviations of SST anomalies over the Niño3.4 region (i.e. 5°S-5°N and 120-170°W) in the CMIP5 models (coloured contours refer to each individual model; cf. Table 1) and in the CRU TS 3.24.01 observations (grey shaded). SST anomalies are here calculated by subtracting the monthly climatology according to the definition of the Niño3.4 index (Trenberth 1997). To reduce the influence of the global trends, SST anomalies are detrended using a locally weighted linear regressions, with span equal to the length of the data. (EPS 153 KB)


  1. AchutaRao K, Sperber KR (2006) ENSO simulation of coupled ocean-atmosphere models: are the current models better?. Clim Dyn 27:1–15CrossRefGoogle Scholar
  2. Allan RJ, Lindesay JA, Reason CJC (2003) Multidecadal variability in the climate system over the Indian Ocean region during the austral summer. J Clim 8:185 – 1873Google Scholar
  3. Ault TR, Cole JE, St. George S (2012) The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models. Geophys Res Lett 39:L21705. CrossRefGoogle Scholar
  4. Ault TR, Deser C, Newman M, Emile-Geay J (2013) Characterizing decadal to centennial variability in the equatorial Pacific during the last millennium. Geophys Res Lett 40:3450–3456CrossRefGoogle Scholar
  5. Ba J, Keenlyside N, Latif M, Park W, Ding H, Lohmann K, Mignot J, Menary M, Ottera O, Wouters B, Salas y Melia D, Oka A, Belluci A, Volodin E (2014) A multi-model comparison for Atlantic multidecadal variability. Clim Dyn 9:2333–2348CrossRefGoogle Scholar
  6. Behera SK, Yamagata Y (2001) Subtropical SST dipole events in the southern Indian Ocean. Geophys Res Lett 28:327–330. CrossRefGoogle Scholar
  7. Beraki AF, DeWitt DG, Landman WA, Olivier C (2014) Dynamical seasonal climate prediction using an ocean-atmosphere coupled climate model developed in partnership between South Africa and the IRI. J Clim 27:1719–1741CrossRefGoogle Scholar
  8. Capotondi A, Wittenberg A, Masina S (2006) Spatial and temporal structure of tropical Pacific interannual variability in 20th century coupled simulations. Ocean Model 15:274–278CrossRefGoogle Scholar
  9. Ciasto LM, Alexander MA, Deser C, England MH (2011) On the persistence of cold-season SST anomalies associated with the annular Mode. J Clim 24:2500–2515CrossRefGoogle Scholar
  10. Compo GP, Whitaker JS, Sardeshmukh PD (2006) Feasibility of a 100 year reanalysis using only surface pressure data. Bull Am Met Soc 87:175–190CrossRefGoogle Scholar
  11. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Brönniman S, Brunet M, Crouthamel RI, Grant AN,. Groisman PY, Jones JD, Kruk M, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli Ø, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28CrossRefGoogle Scholar
  12. Conway D (2015) Climate and southern Africa’s water-energy-food nexus. Nat Clim Change 5:837–846CrossRefGoogle Scholar
  13. Cook KH (2001) A Southern Hemisphere wave response to ENSO with implications for southern Africa precipitation. J Atmos Sci 15:2146–2162CrossRefGoogle Scholar
  14. Crétat J, Richard Y, Pohl B, Rouault M, Reason CJC, Fauchereau N (2012) Recurrent daily rainfall patterns over South Africa and associated dynamics during the core of the austral summer. Int J Climatol 32:261–273CrossRefGoogle Scholar
  15. Crétat J, Pohl B, Dieppois B, Berthou S, Pergaud J (2018) The Angola Low: relationship with southern African rainfall and ENSO. Clim Dyn. CrossRefGoogle Scholar
  16. De Viron O, Dickey JO, Ghil M (2013) Global modes of variability. Geophys Res Lett 40:1832–1837CrossRefGoogle Scholar
  17. Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal climate variability. Clim Dyn 38:527–546CrossRefGoogle Scholar
  18. Di Lorenzo E, Liguori G, Schneider N, Furtado JC, Anderson BT, Alexander MA (2015) ENSO and meridional models: a null hypothesis for Pacific climate variability. Geophys Res Lett 42:9440–9448CrossRefGoogle Scholar
  19. Dieppois B, Rouault M, New M (2015) The impact of ENSO on Southern African rainfall in CMIP5 ocean atmosphere coupled climate models. Clim Dyn 45:2425–2442CrossRefGoogle Scholar
  20. Dieppois B, Pohl B, Rouault M, New M, Lawler D, Keenlyside N (2016) Interannual to interdecadal variability of winter and summer southern African rainfall, and their teleconnections. J Geophys Res Atmos 121:6215–6239CrossRefGoogle Scholar
  21. Dyer TGJ, Tyson PD (1977) Estimating above and below normal rainfall periods over South Africa, 1972–2000. J Appl Meteorol 16:145–147CrossRefGoogle Scholar
  22. Fauchereau N, Trzaska S, Richard Y, Roucou P, Camberlin P (2003) Sea-surface temperature co-variability in the southern Atlantic and Indian Oceans and its connections with the atmospheric circulation in the Southern Hemisphere. Int J Clim 23:663–677CrossRefGoogle Scholar
  23. Fleming LE, Anchukaitis KJ (2016) North Pacific decadal variability in the CMIP5 last millennium simulations. Clim Dyn 47:3783–3801CrossRefGoogle Scholar
  24. Ghil M, Allen MR, Dettinger MD, Ide K, Kondrashov D, Mann ME, Robertson AW, Sauders A, Tian Y, Varadi F, Yiou P (2002) Advanced spectral methods for climatic time series. Rev Geophys 40(3-1-):3–41Google Scholar
  25. Guilyardi E, Wittenberg A, Fedorov A, Collins M, Wang C, Capotondi A, van Oldenborg GJ, Stockdate T (2009) Understanding El Niño in Ocean-atmosphere general circulation models: progress and challenges. Bull Am Meteor Soc 90:325–340CrossRefGoogle Scholar
  26. Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Clim 34:623–642CrossRefGoogle Scholar
  27. Hart NCG, Reason CJC, Fauchereau N (2012) Cloud bands over southern Africa: seasonality, contribution to rainfall variability and modulation by the MJO. Clim Dyn 41:119–1212Google Scholar
  28. Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteor Soc 90:1095–1108CrossRefGoogle Scholar
  29. Henley BJ, Meehl G, Power SB, Folland CK, King A, Brown JN, Karoly DJ, Delage F, Gallant AJE, Freund M, Neukom R (2017) Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ Res Lett 12:044011CrossRefGoogle Scholar
  30. Hermes JC, Reason CJC (2005) Ocean model diagnosis of interannual coevolving SST variability in the South Indian and South Atlantic Oceans. J Clim 18:2864–2882CrossRefGoogle Scholar
  31. Hewitson BC, Crane RG (2006) Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa. Int J Clim 26:1315–1337CrossRefGoogle Scholar
  32. Hiraha S, Ishii M, Fukuda Y (2014) Centennial-scale sea surface temperature analysis and its uncertainty. J Clim 27:57–75CrossRefGoogle Scholar
  33. Hoell A, Cheng L (2018) Austral summer Southern Africa precipitation extremes forced by the El Niño Southern Oscillation and the subtropical Indian Ocean Dipole. Clim Dyn 50:3219–3236CrossRefGoogle Scholar
  34. Hoell A, Funk C, Magadzire T, Zinke J, Husak G (2015) El Niño-Southern oscillation diversity and Southern Africa teleconnections during austral summer. Clim Dyn 45:1583–1599CrossRefGoogle Scholar
  35. Hoell A, Funk C, Zinke J, Harrison L (2017) Modulation of the Southern Africa precipitation response to the El Niño Southern Oscillation by the subtropical Indian Ocean Dipole. Clim Dyn 48:2529–2540CrossRefGoogle Scholar
  36. Hoskins BJ, Ambrizzi T (1993) Rossby Wave propagation on a realistic longitudinally varying flow. J Atmos Sci 50:1661–1671CrossRefGoogle Scholar
  37. Huang B, Banzon VF, Freeman E, Lawrimore J, Liu W, Peterson TC, Smith TM, Thorne PW, Woodruff SD, Zhang HM (2015a) Extended reconstructed sea surface temperature version 4 (ERSST.v4). Part I: upgrades and intercomparisons. J Clim 28:911–930CrossRefGoogle Scholar
  38. Huang B, Thorne PW, Smith TM, Liu W, Lawrimore J, Banzon F, Zhang H-M, Peterson TC, Menne M (2015b) Further exploring and quantifying uncertainties or extended reconstructed sea surface temperature (ERSST) version 4 (v4). J Clim 29:3119–3142CrossRefGoogle Scholar
  39. James R, Washington R, Abiodun B, Kay G, Mutemi J, Pokam W, Hart N, Artan G, Senior C (2018) Evaluating climate models with an African lens. Bull Am Meteor Soc 99:313–336CrossRefGoogle Scholar
  40. Jun M, Knutti R, Nychka DW (2008) Local eigenvalue of CMIP3 climate model errors. Tellus A 60:992–1000CrossRefGoogle Scholar
  41. Jury MK (2014) Factors contributing to a decadal oscillation in South African rainfall. Theor Appl Climatol 120:227–237CrossRefGoogle Scholar
  42. Kalogomou EA, Lennard C, Shongwe M, Pinto I, Favre A, Kent M, Hewitson B, Dosio A, Nikulin G, Panitz H-J, Buchner M (2013) A diagnostic evaluation of precipitation in CORDEX models over Southern Africa. J Clim 26:9477–9506CrossRefGoogle Scholar
  43. Kane RP (2009) Periodicities, ENSO effects and trends of some South African rainfall series: an update. S Afr J Sci 105:199–207Google Scholar
  44. Kay JE, Deser C, Phillips A, Mai A, Hannay C, Strand G, Arblaster JM, Bates SC, Danabasoglu G, Edwards J, Holland M, Kushner P, Lamarque J, Lawrence D, Lindsay K, Middleton A, Munoz E, Neale R, Oleson K, Polvani L, Vertenstein M (2015) The Community earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull Am Meteor Soc 96:1333–1349CrossRefGoogle Scholar
  45. Keenlyside NS, Latif M, Jungclauss J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453:84–88CrossRefGoogle Scholar
  46. Klein SA, Soden BJ, Lau NC (1999) Remote sea surface variations during ENSO: evidence for a tropical atmospheric bridge. J Clim 12:917–932CrossRefGoogle Scholar
  47. Knutti R (2010) The end of model democracy? Clim Change 102:395–404CrossRefGoogle Scholar
  48. Knutti R, Meehl GA, Allen R, Stainforth DA (2006) Constraing climate sensitivity from the seasonal cycle in surface temperature. J Clim 19:4224–4233CrossRefGoogle Scholar
  49. Laepple T, Huybers P (2014) Ocean surface temperature variability: Large model-data differences at decadal and longer periods. Proc Natl Acad Sci 111:16682–16687CrossRefGoogle Scholar
  50. Landman WA, Beraki A (2012) Multi-model forecast skill for mid-summer rainfall over southern Africa. Int J Clim 32:303–314CrossRefGoogle Scholar
  51. Landman WA, Engelbrecht F, Hewitson B, Malherbe J, van der Merwe J (2017) Towards bridging the gap between climate change projections and maize producers in South Africa. Theor Appl Climatol 132:1153–1163CrossRefGoogle Scholar
  52. Lazenby C, Todd M, Wang Y (2016) Climate model simulation of South Indian Ocean Convergence Zone: Mean state and variability. Clim Res 68:59–71CrossRefGoogle Scholar
  53. Lee T, Waliser DE, Li JL, Landerer FW, Gierach MM (2013) Evaluation of CMIP3 and CMIP5 wind stress climatology unising satellite measurements and atmospheric reanalysis products. J Clim 26:5810–5826CrossRefGoogle Scholar
  54. Lim E-P, Hendon HH, Arblastger JM, Delage F, Nguyen H, Min S-K, Wheeler MC (2016) The impact of the Southern Annular Mode on future changes in Southern Hemisphere rainfall. Geophys Res Lett 43:7160–7167CrossRefGoogle Scholar
  55. Lin J-L (2007) The double ITCZ problem in IPCC AR4 couple GCMs: Ocean–atmosphere feedback analysis. J Clim 20:4497–4525CrossRefGoogle Scholar
  56. Lindesay JA (1988) South African rainfall, the Southern Oscillation and a Southern Hemisphere semi-annual cycle. J Climatol 8:17–30CrossRefGoogle Scholar
  57. Lübbecke JF, Böning CW, Keenlyside NS, Xie S-P (2010) On the connection between Benguela and equatorial Atlantic Niños and the role of the South Atlantic Anticyclone. J Geophys Res Oceans 115:C0915. CrossRefGoogle Scholar
  58. Lyon B, Mason SJ (2007) The 1997-98 summer season in southern Africa. Part I: observations. J Clim 20:5134–5148CrossRefGoogle Scholar
  59. Macron C, Pohl B, Richard Y, Bessafi M (2014) How do tropical-temperate troughs form and develop over southern Africa? J Clim 27:1633–1647CrossRefGoogle Scholar
  60. Malherbe J, Landman WA, Engelbrecht FA (2014) The bi-decadal rainfall cycle, Southern Annular Mode and tropical cyclones over the Limpopo River Basin, southern Africa. Clim Dyn 42:3121–3138CrossRefGoogle Scholar
  61. Malherbe J, Dieppois B, Maluleke P, Van Staden M, Pillay DL (2016) South African droughts and decadal variability. Nat Hazards 80:657–681CrossRefGoogle Scholar
  62. Mann ME, Park J (1994) Global modes of surface temperature variability on interannual to century timescales. J Geophys Res-Atmos 99:25819–25833CrossRefGoogle Scholar
  63. Mantua NJ, Hare SR (2002) The Pacific decadal oscillation. J Oceanogr 58:35–44CrossRefGoogle Scholar
  64. Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A pacific interdecadal climate oscillation with impacts on Salmon production. Bull Am Meteor Soc 78:1069–1079CrossRefGoogle Scholar
  65. Mason SJ (1998) Seasonal forecasting of South Africa rainfall using a non-linear discriminant analysis model. Int J Clim 18:147–164CrossRefGoogle Scholar
  66. Mason SJ, Jury M (1997) Climatic variability and change over the Southern Africa: a reflection on underlying processes. Prog Phys Geo 21:23–50CrossRefGoogle Scholar
  67. Menary MB, Hodson DLR, Robson JI, Sutton RT, Wood RA, Hunt JA (2015) Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability. Geophys Res Lett 14:5926–5934CrossRefGoogle Scholar
  68. Morioka Y, Engelbrecht F, Behera S (2015) Potential sources of multidecadal climate variability over southern Africa. J Clim 28:8695–8709CrossRefGoogle Scholar
  69. Munday C, Washington R (2016) Circulation controls on southern African precipitation in coupled models: The role of the Angola Low. J Geophys Res Atmos 122:861–877CrossRefGoogle Scholar
  70. Munday C, Washington R (2018) Systematic climate model rainfall biases over southern Africa: links to moisture circulation and topography. J Clim 31:7533–7548CrossRefGoogle Scholar
  71. Newman M, Alexander MA, Ault T, Cobb KM, Deser C, Di Lorenzo E, Mantua NJ, Miller AJ, Minobe S, Nakamura H, Schneider N, Vimont DJ, Phillips AS, Scott JD, Smith CA (2016) The pacific decadal oscillation, revisited. J Clim 29:4399–4427CrossRefGoogle Scholar
  72. Nicholson SE, Kim J (1997) The relationship of the El Niño-Southern Oscillation to African rainfall. Int J Clim 17:117–135CrossRefGoogle Scholar
  73. Nikulin G, Jones C, Giorgi F, Asrar G, Buchner M, Cerezo-Mota R, Christensen OB, Déqué M, Fernandez J, Hansler A, van Meijgaard E, Samuelsson P, Sylla MB, Sushama L (2012) Precipitaiton climatology in an ensemble of CORDEX-Africa regional climate simulations. J Clim 25:6057–6078CrossRefGoogle Scholar
  74. Pohl B, Dieppois B, Crétat J, Lawler DM, Rouault M (2018) From synoptic to interdecadal variability in southern African rainfall: towards a unified view across timescales. J Clim 31:5845–5872CrossRefGoogle Scholar
  75. Power S, Casey T, Folland CK, Colman A, Mehta V (1999) Inter-decadal modulation of the impact of ENSO on Australia. Clim Dyn 15:319–323CrossRefGoogle Scholar
  76. Power S, Delage F, Wang G, Smith I, Kociuba G (2017) Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections. Clim Dyn 49:53–69CrossRefGoogle Scholar
  77. Ratnam JV, Behera SK, Masumoto Y, Yamagata T (2014) Remote effects of El Niño and Modoki events on the Austral Summer Precipitation of Southern Africa. J Clim 27:3802–3815CrossRefGoogle Scholar
  78. Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res Atmos 108:4407. CrossRefGoogle Scholar
  79. Reason CJC (1998) Warm and cold events in the southeast Atlantic/southwest Indian Ocean region and potential impacts on circulation and rainfall over southern Africa. Meteorog Atmos Phys 69:49–65CrossRefGoogle Scholar
  80. Reason CJC (2001) Subtropical Indian Ocean SST dipole events and southern African rainfall. Geophys Res Lett 28:2225–2227CrossRefGoogle Scholar
  81. Reason CJC, Mulenga H (1999) Relationships between South African rainfall and SST anomalies in the southwest Indian Ocean. Int J Climatol 19:1651–1673CrossRefGoogle Scholar
  82. Reason CJC, Smart S (2015) Tropical south east Atlantic warm events ad associated rainfall anomalies over southern Africa. Front Environ Sci 3:1–11. CrossRefGoogle Scholar
  83. Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625CrossRefGoogle Scholar
  84. Richard Y, Fauchereau N, Poccard I, Rouault M, Trzaska S (2001) XXth century droughts in Southern Africa: spatial and temporal variability, teleconnections with oceanic and atmospheric conditions. Int J Clim 21:873–885CrossRefGoogle Scholar
  85. 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
  86. Ropelewski CF, Halpert MS (1989) Precipitation patterns associated with the high indices phase of the southern oscillation. J Clim 2:268–284CrossRefGoogle Scholar
  87. Rouault M (2012) Bi-annual intrusion of tropical water in the northern Benguela upwelling. Geophys Res Lett 39:L12606. CrossRefGoogle Scholar
  88. Rouault M, Richard Y (2005) Intensity and spatial extent of droughts in Southern Africa. Geophys Res Lett 32:L15702. CrossRefGoogle Scholar
  89. Sarachick ES, Cane MA (2010) The El Niño-Southern Oscillation Phenomenom. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  90. Schneider N, Cornuelle BD (2005) The forcing of the Pacific Decadal Oscillation. J Clim 18:4355–4373CrossRefGoogle Scholar
  91. Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2014) 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 115:15–40CrossRefGoogle Scholar
  92. Shimodaira H (2002) An approximately unbiased tests of phylogenetic tree selection. Syst Biol 51:492–508CrossRefGoogle Scholar
  93. Shimodaira H (2004) Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling. Ann Stat 32:2616–2641CrossRefGoogle Scholar
  94. Taylor KE, Stouffler RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  95. Todd MC, Washington R (1999) Circulation anomalies associated with tropical-temperate troughts in southern Africa and the southwest Indian Ocean. Clim Dyn 15:937–951CrossRefGoogle Scholar
  96. Tourre YM, Kushnir Y, White WB (1999) Evolution of interdecadal variability in sea level pressure, sea surface temperature, and upper ocean temperature over the Pacific Ocean. J Phys Oceanogr 9:1528–1541CrossRefGoogle Scholar
  97. Tourre YM, Rajagopalan B, Kushnir Y, Barlow M, White WB (2001) Patterns of coherent ocean decadal and interdecadal climate signals in the Pacific basin during the 20th century. Geophys Res Lett 28:2069–2072CrossRefGoogle Scholar
  98. Tourre YM, Cibot C, Terray L, White WB, Dewitte B (2005) Quasi-decadal and inter-decadal climate fluctuations in the Pacific Ocean from a CGCM. Geophys Res Lett 32:L07710. CrossRefGoogle Scholar
  99. Trenberth K (1997) The Definition of El Niño. Bull Am Meteorol Soc 78:2771–2778CrossRefGoogle Scholar
  100. Tyson PD (1981) Atmospheric circulation variations and the occurrence of extended wet and dry spells over southern Africa. J Climatol 1:115–130CrossRefGoogle Scholar
  101. Tyson PD (1986) Climatic Change and variability over southern Africa. Oxford University Press, Cape TownGoogle Scholar
  102. Venegas S, Mysak LA, Straub DN (1997) Atmosphere-ocean coupled variability in the South Atlantic. J Clim 10:2904–2920CrossRefGoogle Scholar
  103. Vigaud N, Richard Y, Rouault M, Fauchereau N (2009) Moisture transport between the South Atlantic Ocean and southern Africa: relationships with summer rainfall and associated dynamics. Clim Dyn 32:113–123CrossRefGoogle Scholar
  104. Ward JH (1963) Hierarchical grouping to optimize an objective fundion. J Am Stat Assoc 58:236–244CrossRefGoogle Scholar
  105. Washington R, Preston A (2006) Extreme wet years over southern Africa: role of Indian Ocean sea surface temperatures. J Geophys Res Atmos 111:D15104. CrossRefGoogle Scholar
  106. Wu Z, Huang NE, Long SR, Peng C-K (2007) On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci USA 104:14889–14894CrossRefGoogle Scholar
  107. Yue S, Wang C (2004) The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag 18:201–218CrossRefGoogle Scholar
  108. Zhang Q, Kornich H, Holmgren K (2013) How well do reanalyses represent the southern African precipitation. Clim Dyn 40:951–962CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Centre for Agroecology, Water and Resilience (CAWR)Coventry UniversityCoventryUK
  2. 2.Department of Oceanography, MARE InstituteUniversity of Cape TownCape TownSouth Africa
  3. 3.School of Geography, Earth and Environmental SciencesUniversity of BirminghamBirminghamUK
  4. 4.Centre de Recherches de ClimatologieUMR 6282 Biogéosciences, CNRS/Université de Bourgogne Franche ComtéDijonFrance
  5. 5.IPSL/Laboratoire des Sciences du Climat et de l’EnvironnmentCEA-CNRS-UVSQ, Université Paris SaclayGif-sur-YvetteFrance
  6. 6.African Climate and Development InitiativeUniversity of Cape TownCape TownSouth Africa
  7. 7.Nansen-Tutu Center for Marine Environmental ResearchUniversity of Cape TownCape TownSouth Africa

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