Theoretical and Applied Climatology

, Volume 99, Issue 1–2, pp 9–27 | Cite as

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

  • C. J. R. Williams
  • D. R. Kniveton
  • R. Layberry
Original Paper


It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.


Daily Rainfall Rain Rate Rainfall Extreme Rainfall Variability Global Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Adler R, Kidd C, Petty G, Morissey M, Goodman H (2001) Intercomparison of global precipitation products: the third Precipitation Intercomparison Project (PIP-3). Bull Am Meteorol Soc 82(7):1377–1396CrossRefGoogle Scholar
  2. Adler R, Negri A, Keehn P, Hakkarinen I (1993) Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. J Appl Meteorol 32:335–356CrossRefGoogle Scholar
  3. Arkin P, Meisner B (1987) The relationship between large scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon Weather Rev 115:51–74CrossRefGoogle Scholar
  4. Bartman A, Landman W, Rautenbach C (2003) Recalibration of general circulation model output to austral summer rainfall over Southern Africa. Int J Climatol 23(12):1407–1419CrossRefGoogle Scholar
  5. Bellerby T, Todd M, Kniveton D, Kidd C (2000) Rainfall estimation from a combination of TRMM precipitation radar and GOES multi-spectral imagery through the use of an artificial neural network. J Appl Meteorol 39(12):2115–2128CrossRefGoogle Scholar
  6. CGAM (2006). ‘Introduction to the UM’. Unified Model Information Service. 6/2/07
  7. Cook K (2000) The South Indian Convergence Zone and interannual rainfall variability over southern Africa. J Climate 13(21):3789–3804CrossRefGoogle Scholar
  8. Davies H, Turner R (1977) Updating prediction models by dynamical relaxation—examination of technique. Q J R Meteorol Soc 103(436):225–245CrossRefGoogle Scholar
  9. Ebert E, Manton M, Arkin P, Allam R, Holpin G, Gruber A (1996) Results from the GPCP algorithm intercomparison programme. Bull Am Meteorol Soc 77(12):2875–2887CrossRefGoogle Scholar
  10. ECMWF (2003). ‘ERA’. European Centre for Medium-Range Weather Forecasts. 6/2/07
  11. Fauchereau N, Trzaska S, Rouault M, Richard Y (2003) Rainfall variability and changes in southern Africa during the 20th century in the global warming context. Nat Hazards 29(2):139–154CrossRefGoogle Scholar
  12. Goddard L, Graham N (1999) Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J Geophys Res 104(D16):19009–19116CrossRefGoogle Scholar
  13. Hudson D (2002) Future changes in temperature and precipitation extremes over southern Africa. DEFRA Report 2/2/02. The Meteorological Office, Hadley Centre for Climate Prediction and Research, UKGoogle Scholar
  14. Hudson D, Jones R (2002a) Simulations of present-day and future climate over southern Africa using HadAM3H. Hadley Centre technical note 38. The Meteorological Office, Hadley Centre for Climate Prediction and Research, UK, pp 37Google Scholar
  15. Hudson D, Jones R (2002b) Regional climate model simulations of present-day and future climates of southern Africa. Hadley Centre technical note 39. The Meteorological Office, Hadley Centre for Climate Prediction and Research, UK, pp 40Google Scholar
  16. Huffman G, Adler R, Arkin P, Chang A, Ferraro R, Gruber A, Janowiak J, McNab A, Rudolf B, Schneider U (1997) The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull Am Meteorol Soc 78(1):5–20CrossRefGoogle Scholar
  17. Jones R, Noguer M, Hassell D, Hudson D, Wilson S, Jenkins G, Mitchell J (2004) Generating high resolution climate change scenarios using PRECIS. The Meteorological Office, Hadley Centre for Climate Prediction and Research, Bracknell, p 40Google Scholar
  18. Joyce R, Janowiak J, Arkin P, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5(3):487–503CrossRefGoogle Scholar
  19. Landman W, Mason S, Tyson P, Tennant W (2001) Retro-active skill of multi-tiered forecasts of summer rainfall over southern Africa. Int J Climatol 21(1):1–19CrossRefGoogle Scholar
  20. Layberry R, Kniveton D, Todd M, Kidd C, Bellerby T (2006) Daily precipitation over southern Africa: a new resource for climate studies. J Hydrometeorol 7(1):149–159CrossRefGoogle Scholar
  21. Miller S, Arkin P, Joyce R (2001) A combined microwave/infrared rain rate algorithm. Int J Remote Sens 22(17):3285–3307CrossRefGoogle Scholar
  22. Meehl G, Stocker T, Collins W, Friedlingstein P, Gaye A, Gregory J, Kitoh A, Knutti R, Murphy J, Noda A, Raper S, Watterson I, Weaver A, Zhao Z-C (2007) Global climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of working group I to the Fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 996Google Scholar
  23. Nicholson S (2003) Comments on “The South Indian Convergence Zone and interannual rainfall variability over southern Africa” and the question of Enso’s influence on southern Africa. J Climate 16(3):555–562CrossRefGoogle Scholar
  24. Nicholson S, Kim J (1997) The relationship of the El Niño–Southern Oscillation to African rainfall. Int J Climatol 17:117–135CrossRefGoogle Scholar
  25. Rautenbach C, Smith I (2001) Teleconnections between global sea-surface temperatures and the interannual variability of observed and model simulated rainfall over southern Africa. J Hydrol 254(1–4):1–15CrossRefGoogle Scholar
  26. Reason C (1998) Warm and cold events in the southeast Atlantic/southwest Indian Ocean region and potential impacts on circulation and rainfall over southern Africa. Meteorol Atmos Phys 69:49–65CrossRefGoogle Scholar
  27. Richard Y, Poccard I (1998) A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. Int J Climatol 19(15):2907–2920Google Scholar
  28. Ropelewski C, Halpert M (1987) Global and regional scale precipitation patterns associated with the El-Niño Southern Oscillation. Mon Weather Rev 115:1606–1626CrossRefGoogle Scholar
  29. Samel A, Wang W, Liang X (1999) The monsoon rainband over China and relationships with the Eurasian circulation. J Climate 12(1):115–131CrossRefGoogle Scholar
  30. Sorooshian S, Gao X, Hsu K, Maddox R, Hong Y, Gupta H, Imam B (2002) Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information. J Climate 15:983–1001CrossRefGoogle Scholar
  31. Thiam E, Singh V (2002) Space–time–frequency analysis of rainfall, runoff and temperature in the Casamance River basin, southern Senegal, West Africa. Water SA 28(3):259–270Google Scholar
  32. Todd M, Kidd C, Kniveton D, Bellerby T (2001) A combined satellite infrared and passive microwave technique for estimation of small scale rainfall. J Atmos Ocean Technol 18(5):742–755CrossRefGoogle Scholar
  33. Van der Wal A (1998) The Unified Model. Unified Model user guide. Version 2. The Meteorological Office, Bracknell, p 192Google Scholar
  34. Williams C, Kniveton D, Layberry R (2007) Climatic and oceanic associations with daily rainfall extremes over southern Africa. Int J Climatol 27(1):93–108CrossRefGoogle Scholar
  35. Xu L, Gao X, Sorooshian S, Arkin P, Imam B (1999) A microwave infrared threshold technique to improve the GOES Precipitation Index. J Appl Meteorol 38:569–579CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • C. J. R. Williams
    • 1
  • D. R. Kniveton
    • 2
  • R. Layberry
    • 3
  1. 1.Department of Meteorology, NCAS-Climate, The Walker Institute for Climate System ResearchUniversity of Reading, Earley GateReadingUK
  2. 2.Department of GeographyUniversity of SussexFalmerUK
  3. 3.Environmental Change InstituteUniversity of OxfordOxfordUK

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