Climate changes in East Africa

Published: 30th March 2011 11:35Last Updated: 30th March 2011 10:35

Introduction

The average global surface temperature has warmed 0.8°C in the past century and 0.6°C in the past three decades (Hansen et al., 2006), in large part because of human activities (IPCC, 2007). The Intergovernmental Panel on Climate Change (IPCC) has projected that if greenhouse gas emissions, the leading cause of climate change, continue to rise, the mean global temperatures will increase by between 1.4 and 5.8°C by the end of the 21st century (IPCC, 2007).

Climate change impacts have the potential to undermine and even, undo progress made in improving the socio-economic well-being of many of the African countries. The negative impacts associated with climate change are also compounded by many factors, including widespread poverty, human diseases, and high population density, which is estimated to double the demand for food, water, and livestock forage within the next 30 years.

The countries of Eastern Africa are prone to extreme climatic events such as droughts and floods. In the past, these events have had severe negative impacts on key socioeconomic sectors of the economies of most countries in the sub region. In the late seventies and eighties, droughts caused widespread famine and economic hardships in many countries of the sub region. There is evidence that future climate change may lead to a change in the frequency or severity of such extreme weather events, potentially worsening these impacts. In addition, future climate change will lead to increases in average mean temperature and sea level rise, and changes in annual and seasonal rainfall. These will have potentially important effects across all economic and social sectors in the region, possibly affecting agricultural production, health status, water availability, energy use, biodiversity and ecosystem services (including tourism). Any resulting impacts are likely to have a strong distributional pattern and amplify inequities in health status and access to resources, as vulnerability is exacerbated by existing developmental challenges, and because many groups (e.g. rural livelihoods) will have low adaptive capacity.

East Africa is characterised by widely diverse climates ranging from desert to forest over relatively small areas. Rainfall seasonality is complex, changing within tens of kilometres. Altitude is also an important contributing factor. The annual cycle of East African rainfall is bimodal, with wet seasons from March to May and October to December. The Long Rains (March to May) contribute more than 70% to the annual rainfall and the Short Rains less than 20%. Much of the interannual variability comes from Short Rains (coefficient of variability = 74% compared with 35% for the Long Rains) (WWF, 2006).

Regional historic climate trends

Results from recent work from stations in Kenya and Tanzania, indicate that since 1905, and even recently, the trend of daily maximum temperature is not significantly different from zero. However, daily minimum temperature results suggest an accelerating temperature rise (Christy et al., 2009).

A further study looking at day and night temperatures concluded that the northern part of East Africa region generally indicated nighttime warming and daytime cooling in recent years. The trend patterns were, however, reversed at coastal and lake areas. There were thus large geographical and temporal variations in the observed trends, with some neighbouring locations at times indicating opposite trends. A significant feature in the temperature variability patterns was the recurrence of extreme values. Such recurrences were significantly correlated with the patterns of convective activities, especially El Niño-Southern Oscillation (ENSO), cloudiness, and above/below normal rainfall.

During recent decades, eastern Africa has been experiencing an intensifying dipole rainfall pattern on the decadal time-scale. The dipole is characterised by increasing rainfall over the northern sector and declining amounts over the southern sector (Schreck and Semazzi, 2004).

East Africa has suffered both excessive and deficient rainfall in recent years (Webster et al., 1999, Hastenrath et al., 2007). In particular, the frequency of anomalously strong rainfall causing floods has increased. Shongwe, van Oldenborgh and Aalst (2009) report that their analysis of data from the international Disaster Database (EM-DAT shows that there has been an increase in the number of reported hydrometeorological disasters in the region, from an average of less than 3 events per year in the 1980s to over 7 events per year in the 1990s and 10 events per year from 2000 to 2006, with a particular increase in floods. In the period 2000-2006 these disasters affected on average almost two million people per year.

Historic context of climate extremes in East Africa:

  • Large variability in rainfall with occurrence of extreme events in terms of droughts and floods.
  • Droughts in the last 20 years -1983/84, 1991/92, 1995/96, 1999/2001, 2004/2005 (led to famine).
  • El-Niño related floods of 1997/98 was a very severe event enhanced by unusual pattern of SST in the Indian Ocean (IPCC, 2007).
  • The La Niña related drought of 1999/2001.

The El Niño in 1997/98 and La Niña in 1999/2000 were the most severe in 50 years.

Regional climate variability

Recent research suggests that warming sea surface temperatures, especially in the southwest Indian Ocean, in addition to inter-annual climate variability (i.e., El Niño/Southern Oscillation (ENSO)) may play a key role in East African rainfall and may be linked to the change in rainfall across some parts of equatorial-subtropical East Africa (Cane et al., 1986; Plisnier et al., 2000; Rowe, 2001). Warm sea surface temperatures are thought to be responsible for the recent droughts in equatorial and subtropical Eastern Africa during the 1980s to the 2000s (Funk et al., 2005). According to the U.N. Food and Agriculture Organization (FAO, 2004), the number of African food crises per year has tripled from the 1980s to 2000s. Drought diminished water supplies reduce crop productivity and have resulted in widespread famine in East Africa.

El Niño is the most important factor in interannual variability of precipitation in East Africa.

The Indian and Atlantic Oceans also play a role. Local geographic factors may complicate the impact of large-scale factors. There have been relatively few recent studies of rainfall variability in East Africa, compared with areas such as the Sahel. Even fewer studies exist of Indian Ocean variability and its impact on the climate.

Interannual variability of rainfall is remarkably coherent within a region. The Short Rains, in particular, are characterised by greater spatial coherence and are linked more to large scale than regional factors. The Long Rains (March to May) contribute more than 70% to the annual rainfall and the Short Rains less than 20%. Much of the interannual variability comes from Short Rains (coefficient of variability = 74% compared with 35% for the Long Rains). As a result, the Short Rains are more predictable at seasonal time scales than the Long Rains.

Work on East African climate is focused on rainfall variability but is thinly spread amongst mechanisms of mean climate control, circulation relationships to rainfall variability, the role of ocean patterns (including ENSO and Indian Ocean Dipole (IOD)) in rainfall variability, and the representation of rainfall in regional and global models and the predictability of rainfall. There has also been research on East African lake variability; for example the 1961-1962 rains caused rapid rises in the levels of east African lakes. Lake Victoria rose 2 m in little more than a year (Flohn and Nicholson, 1980). This was not an ENSO year, but exceedingly high sea-surface temperatures (SSTs) occurred in the nearby Indian Ocean as well as the Atlantic.

ENSO comprises two opposite extremes, El Niño and La Niña. El Niño is associated with anomalously wet conditions during the Short Rains and some El Niño events, such as 1997, with extreme flooding. The IOD is regarded as a separate pattern of ocean-based variability although IOD events have occurred together with ENSO leading to extreme conditions over East Africa (e.g. 1982, 1997, 1994, see Figure 2). La Niña conditions are associated with unusually dry conditions over East Africa (Figure 3) during the Short Rains, although the relationship is less reliable than that for El Niño (taken from Downing et al., Final Report Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK).

 

Figure 1 Satellite derived Short rain anomalies in mm/day (i.e. differences from the long term mean) for October-December 1982, 1994, 1997 (La Nina) (taken from Downing et al., Final Report Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK) 

Figure 1 Satellite derived Short rain anomalies in mm/day (i.e. differences from the long term mean) for October-December 1982, 1994, 1997 (La Nina) (taken from Downing et al., Final Report Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK)

 

Figure 2 Satellite derived Short rain anomalies in mm/day (i.e. differences from the long term mean) for October-December 1988, 1998, 2000 (El Nino) (taken from Downing et al., Final Report Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK).

Figure 2 Satellite derived Short rain anomalies in mm/day (i.e. differences from the long term mean) for October-December 1988, 1998, 2000 (El Nino) (taken from Downing et al., Final Report Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK).

Regional scenario projections

Although there have been studies of Global Climate Models (GCM)-simulated climate change for several regions in Africa, the downscaling of GCM outputs to finer spatial and temporal scales has received relatively little attention in Africa. The result of on attempt at the use of a regional model for East Africa is present below following the results of the AR4 climate change scenarios. This work was done as part of the DFID funded work on Climate Screening for Kenya (Downing et al., 2007).

For the application of the Special Report on Emissions Scenarios (SRES) scenarios using the AR4 GCMs there are limitations which apply more to uncertainties in rainfall than temperature projections. The models have not been closely evaluated over the Kenya region, so each of the 8 GCMs used is given equal weight. Model resolution is coarse (c.200km) and the data cannot be applied to the sub-regional scale (e.g. Mount Kenya). It is best to applied over broad regions.

Results from AR4 climate change scenarios

Regardless of the SRES scenario, decade, season or model, all the data points to a warmer future. No simulation shows temperatures cooler than present. The A2 scenario produces warming of around 4 degrees by the end of the century in both seasons. Warming of one degree or less is more typical by 2020.

Almost all the simulations show wetter conditions in October to December, even by 2020. Wetter conditions in Kenya, especially in the Short Rains and especially in northern Kenya (where rainfall increases by 40% by the end of the century) are likely. Analysis of the northern Kenya region show that the increase in seasonal total rainfall in the Short Rains occurs by means of a trend of increasing rainfall extremes which, in models like MPI, are evident from the outset of the 21st Century. At the same time the droughts remain as extreme as present, even increasing in intensity through the 21st Century.

There is little change in the timing of the seasonal variations for either rainfall or temperature over future decades.

In brief, climate model experiments using AR4 climate scenarios (IPCC, 2007) based on the gridded model data show that:

  • East African climate is likely to become wetter, particularly in the Short Rains (October to December) and particularly in northern Kenya, in the forthcoming decades.
  • East Africa will almost certainly become warmer than present in all seasons in the forthcoming decades.
  • Changes in rainfall seasonality over forthcoming decades are unlikely.
  • A trend towards more extremely wet seasons is likely for the Short Rains, particularly in northern Kenya, in the forthcoming decades.
  • Droughts are likely to continue (notwithstanding the generally wetter conditions), particularly in northern Kenya, in the forthcoming decades. In many model simulations, the drought events every 7 years or so become more extreme than present.
  • The wetting component evident in observed Kenyan rainfall may well be a forerunner of the longer term climate change.

Regional climate change modelling (RegCM3)

Regional climate simulation for the East Africa region has so far been confined to one model and one emission scenario (A2) so the results are very uncertain (Downing et al., 2006). To improve the certainty it would need multiple regional models and emission scenarios - a modelling effort which amounts to years more work. Ideally, future regional downscaling of the global climate projections for Kenya should be extended to other IPCC GCMs, to gain a better sense of the uncertainty associated with the regional climate model projections.

The regional climate projections indicate that the role of sharp mountain range slopes, such as the Great Rift Valley in Kenya, can greatly affect local climate. The IPCC GCMs are based on a large grid resolution (200 x 200km 2) and do not include modifications for altitude. GCM projections are valuable for projections on the large scale, as long as they are interpreted with caution, particularly when large contrasts in altitude exist over short distances like in Kenya.

Results from the North Carolina State University enhanced version of the RegCM3 regional model (Anyah et al, 2006) which were run for both a control and one climate change (A2 scenario) simulation, have been analysed for Kenya. A domain resolution of 20 km forms the basis of these experiments. These class of models offer much higher resolution than the GCMs and, as a result, are of relevance to complex terrain which characterizes Kenya. The regional model was forced by global fields from the FvGCM model.

  • Climate analysis using the Regional GCM model indicates that Kenya is likely to experience the following climate changes between the late 2020s and 2100:
  • Average annual temperature will rise by between 1°C and 5C, typically 1°C by 2020s and 4°C by 2100.
  • Climate is likely to become wetter in both rainy seasons, but particularly in the Short Rain(October to December). Global Climate Models predict increases in northern Kenya (rainfall increases by 40% by the end of the century), whilst a regional model suggests that there may be greater rainfall in the West.
  • The rainfall seasonality i.e. Short and Long Rains are likely to remain the same.
  • Rainfall events during the wet seasons will become more extreme by 2100. Consequently flood events are likely to increase in frequency and severity.
  • Droughts are likely to occur with similar frequency as at present, but to increase in severity.
  • This is linked to the increase in temperature.
  • The Intergovernmental Panel on Climate Change (IPCC) predict an 18 to 59 cm rise in sea- level globally by 2100. One study suggests that 17% of Mombasa's area could be submerged by a sea-level rise of 30 cm (Orindi and Adwera, 2008).

Figure 3 REGCM3 projection results for 2071 to 2100 (A2 RF, 20km resolution) for four seasons - rainfall 

Figure 3 REGCM3 projection results for 2071 to 2100 (A2 RF, 20km resolution) for four seasons - rainfall

Extreme events projections

The IPCC 4th Assessment reports that GCMs project that increasing atmospheric concentrations of greenhouse gases will result in changes in daily, seasonal, inter-annual, and decadal variability. There is projected to be a decrease in diurnal temperature range in many areas, with nighttime lows increasing more than daytime highs. Current projections show little change or a small increase in amplitude for El Niño events over the next 100 years. Many models show a more El Niño-like mean response in the tropical Pacific, with the central and eastern equatorial Pacific sea surface temperatures projected to warm more than the western equatorial Pacific and with a corresponding mean eastward shift of precipitation. Even with little or no change in El Niño strength, global warming is likely to lead to greater extremes of drying and heavy rainfall and increase the risk of droughts and floods that occur with El Niño events in many different regions. There is no clear agreement between models concerning the changes in frequency or structure of other naturally occurring atmosphere-ocean circulation pattern such as the North Atlantic Oscillation (NAO).

Philip and van Oldenborgh (2006) have used climate model simulations from the fourth IPCC Assessment Report (4AR) to investigate changes in ENSO events. The models that simulate El Niño most realistically on average do not show changes in the mean state that resemble the ENSO pattern. The projected changes in amplitude are similar to the observed variability over the last 150 years.

Over much of Kenya, Uganda, Rwanda, Burundi and southern Somali there are indications for an upward trend in rainfall under global warming. Wet extremes (defined as high rainfall events occurring once every 10 years) are projected to increase during both the September to December (SOND) rain season and the March to May (MAM) rain season, locally referred to as the short- and long-rains, respectively (Shongwe, M.E., van Oldenborgh and van Aalst (2009)). In general, a positive shift in the whole rainfall distribution is simulated by the models over most of east Africa during both rainy seasons. However, less confidence is placed on the MAM simulations as the signal-to-noise ratios in the model predictions during this season are relatively low.

Further reading

Anyah, R.O. and Semazzi, F.H.M. (2006).Variability of East African rainfall based on multiyear REGCM3 simulations. International Journal of Climatology, 27, 357-371.

Christy, J.R. W.B. Norris, and R.T. McNider. (2009): Surface Temperature Variations in East Africa and Possible Causes. J. Climate, in press

Clare Downing, Felix Preston, Diana Parusheva, Lisa Horrocks, Oliver Edberg, Frederick Samazzi, Richard Washington, Martin Muteti, Paul Watkiss, Wilfred Nyangena. 2008. Final Report - Appendices, Kenya: Climate Screening and Information Exchange. AEA Technology plc. UK

Cubash U., Meehl, G. A., Boer, G. J., Stouffer, R. J. Dix, M., Noda A., Senior C. A., Raper S., Rap K. S. (2001): Projections of future climate change. Climate changes: The Scientific basis, Cambridge University Press, 525-582.

Hewitson, B.C.; Crane, R.G., (2006). Consensus between GCM climate change projections with empirical downscaling. International Journal of Climatology, 26 (10): 1315 - 1337.

IPCC (2001): Climate Change 2001: Synthesis report, Cambridge University Press, Cambridge.

IPPC (2001a): Climate Change 2001: Impacts, Adaptation and Vulnerability. Cambridge University Press, Cambridge.

IPCC (2007): Climate Change 2007: The Physical Science Basis, IPCC Secretariat, Geneva, Switzerland.

Ogallo, L. A. (1989): The Teleconnections between the global seas surface temperatures and seasonal rainfall over East Africa. J. Japan Met. Soc., Vol. 66, No. 6, 807 822.

Ogallo L. A. (1993): Dynamics of Climate Change over Eastern African Proc. Indian Academy of Sciences, 102, 1, 203 217.

Philip, S.Y. and G.J. van Oldenborgh, 2009. Shifts in ENSO coupling processes under global warming. J. Climate, 22, 14, 4014-4028

Schreck, C.J. and F.H.M Semazzi. (2004), Variability of the recent climate of eastern Africa,International Journal of Climatology, 24 (6): 681-701.

Shongwe, M.E., van Oldenborgh and van Aalst (2009). Projected changes in mean and extreme precipitation in Africa under global warming, Part II: East Africa. Nairobi, Kenya, 56 pp.

WWF-World Wide Fund For Nature (2006). Climate Change Impacts on East Africa. A Review of the Scientific Literature.