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

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Abstract

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.

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References

  • 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):1377–1396

    Article  Google Scholar 

  • 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 32:335–356

    Article  Google Scholar 

  • 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 115:51–74

    Article  Google Scholar 

  • 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):1407–1419

    Article  Google Scholar 

  • 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):2115–2128

    Article  Google Scholar 

  • CGAM (2006). ‘Introduction to the UM’. Unified Model Information Service. http://www.cgam.nerc.ac.uk/um/docs/introduction.php. 6/2/07

  • Cook K (2000) The South Indian Convergence Zone and interannual rainfall variability over southern Africa. J Climate 13(21):3789–3804

    Article  Google Scholar 

  • Davies H, Turner R (1977) Updating prediction models by dynamical relaxation—examination of technique. Q J R Meteorol Soc 103(436):225–245

    Article  Google Scholar 

  • 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):2875–2887

    Article  Google Scholar 

  • ECMWF (2003). ‘ERA’. European Centre for Medium-Range Weather Forecasts. http://www.ecmwf.int/research/era/ERA-15/index.html. 6/2/07

  • 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):139–154

    Article  Google Scholar 

  • Goddard L, Graham N (1999) Importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J Geophys Res 104(D16):19009–19116

    Article  Google Scholar 

  • 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, UK

  • 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 37

  • 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 40

  • 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):5–20

    Article  Google Scholar 

  • 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 40

    Google Scholar 

  • 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):487–503

    Article  Google Scholar 

  • 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):1–19

    Article  Google Scholar 

  • 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):149–159

    Article  Google Scholar 

  • Miller S, Arkin P, Joyce R (2001) A combined microwave/infrared rain rate algorithm. Int J Remote Sens 22(17):3285–3307

    Article  Google Scholar 

  • 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 996

    Google Scholar 

  • 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):555–562

    Article  Google Scholar 

  • Nicholson S, Kim J (1997) The relationship of the El Niño–Southern Oscillation to African rainfall. Int J Climatol 17:117–135

    Article  Google Scholar 

  • 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):1–15

    Article  Google Scholar 

  • 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 69:49–65

    Article  Google Scholar 

  • 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):2907–2920

    Google Scholar 

  • Ropelewski C, Halpert M (1987) Global and regional scale precipitation patterns associated with the El-Niño Southern Oscillation. Mon Weather Rev 115:1606–1626

    Article  Google Scholar 

  • Samel A, Wang W, Liang X (1999) The monsoon rainband over China and relationships with the Eurasian circulation. J Climate 12(1):115–131

    Article  Google Scholar 

  • 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 15:983–1001

    Article  Google Scholar 

  • 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):259–270

    Google Scholar 

  • 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):742–755

    Article  Google Scholar 

  • Van der Wal A (1998) The Unified Model. Unified Model user guide. Version 2. The Meteorological Office, Bracknell, p 192

    Google Scholar 

  • Williams C, Kniveton D, Layberry R (2007) Climatic and oceanic associations with daily rainfall extremes over southern Africa. Int J Climatol 27(1):93–108

    Article  Google Scholar 

  • 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 38:569–579

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Meteorology, NCAS-Climate, The Walker Institute for Climate System Research, University of Reading, Earley Gate, Room 2U18, Reading, RG6 6BB, UK

    C. J. R. Williams

  2. Department of Geography, University of Sussex, Falmer, East Sussex, UK

    D. R. Kniveton

  3. Environmental Change Institute, University of Oxford, Oxford, UK

    R. Layberry

Authors
  1. C. J. R. Williams
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  2. D. R. Kniveton
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  3. R. Layberry
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Correspondence to C. J. R. Williams.

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Williams, C.J.R., Kniveton, D.R. & Layberry, R. Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa. Theor Appl Climatol 99, 9–27 (2010). https://doi.org/10.1007/s00704-009-0124-y

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  • DOI: https://doi.org/10.1007/s00704-009-0124-y

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