reference anchor since temperature increase is often used as an indicator of climate change, and as such impacts ordered by “temperature increase”
rather than “time” allow for better definitions of policy targets (Müller 2009). However, the above limitations and uncertainties remain. Another option will be to use the observed changes (see Section 2.3.1) as a reference since the projected changes (Annex 9 to 17) point in the same direction as the observed changes. What is certain is that temperatures will continue to increase in a “business as usual” development pathway.
Under these circumstances, it can be concluded that future climate change is expected tointensify the already observed changesif no remedial actions are taken to reduce GHG emissions. Yet the limitations of current projec-tions have implicaprojec-tions for adaptation research, planning and financing.
2.4 Implications of climate change projections for adaptation
The uncertainties in future climate change as shown in the Section 2.3.2
ties also arise from the inadequate data basis, the differing and uncertain scenarios of GHG emissions, the coarse spatial and temporal resolutions, contentious assumptions in the emission scenarios and the non-considera-tion of seasons as they occur in reality (e. g. wet/dry season). Further, there are no standard model specifications of input parameters and internal variations exist in the General Circulation Models (GCM) resulting in differing projections of future climates (Müller 2009). These characteris-tics, which are also limitations, can be summarised as follows:
1. Limited (digitised) climate data: In comparison to other parts of the world, meteorological records for Africa are short and there are few stations with long-term records, while many cease to be active or only record climate data intermittently. This affects the quality of climate records that can be used for climate research on Africa. Where cli-mate data exists, it still needs to be digitized before they can be used in modelling.
2. Rainfall poorly captured: What is noteworthy for SSA is that while the GCMs can project past temperatures relatively well, the reliability of the projected future climate change is uncertain. Rainfall which is a key climate variable in Africa and on which most African liveli-hoods depend on is poorly captured in the models (cf. Gleckler et al.
2008; Randall et al. 2007).
3. Coarse temporal resolution of global models: The unit of 3 months used for simulations does not depict the wet and dry seasons as ex-perienced in the SSA regions and this makes for a poor basis for de-veloping agricultural impact models. Only few assessments exist that use the length of the wet season (cf. Marengo et al. 2003; Zhao / Camberlin / Richard 2005)
4. Coarse spatial resolution of global models: The shortcomings of GCMs are acknowledged in the IPCC reports (cf. Randall et al. 2007;
Christensen et al. 2007, 852 ff.; Boko et al. 2007, 458). Randall et al.
(2007) report that while observed temperature is well modelled, mod-els of precipitation deviate greatly from that observed. This deviation is attributed to the nature of African rainfall, which is driven by Sea Surface Temperatures (SSTs), tropical cyclones and the difficulties of the GCMs to represent the interannual variability of SSTs (see also Müller 2009). Yet, the accuracy of projected climate change impacts depends on the accuracy of the projected climate change.
5. Lack of regional models: Reducing the spatial resolution of the GCMs to regional models is one way to provide tailored data for the
SSA region and to validate projections made at coarser scales. Yet studies on South Africa show that there are considerable variations between predictions of average temperature between the global mod-els and local climate modmod-els (Craig / Sharp 2000; Turpie et al. 2000).
For the rest of SSA such regionalised climate change studies are few to inexistent.
6. Quantification units of changes and impacts: Burton / Lim (2005, 193) note that projections are mainly expressed in terms of changes in mean temperature and precipitation. They argued that "the scale and the variables used in global climate models (GCMs) are not those most relevant to choices in the agricultural sector", but many climate models continue to use such scales and variables. Farmers and deci-sion makers need information on short-term seasonal, intra-seasonal, and intra/inter-annual variability but climate change projections are not yet providing this information for SSA. These limitations, for ex-ample, the misfit of the temporal scales in which climate data is cap-tured and disseminated to the public on the one hand, and the tempo-ral scales of interest to farmers, also pertains to the seasonal forecasts of Early Warning Systems (EWS; Ingram / Roncoli / Kirshen 2002;
Ifejika Speranza 2006b). Thus climate change projections are facing similar limitations like the EWS.
7. Emissions Scenarios do not depict the context of many SSA countries:
An examination of the Special Report on Emissions Scenarios (SRES) characteristics used for the projections shows that these rarely depict the African situation or account for the various trends of socio-economic development pathways of SSA: Many SSA countries have a high population growth, low GDP growth, low energy use (World Bank 2007a), medium to high land use changes, low avail-ability of conventional and unconventional oil and gas (with few ex-ceptions), and a low pace of technological change. This currently de-picts a rather low emission scenario. However, if future development in Africa becomes faster and follows a "business as usual" develop-ment path with practices that favour high CO2 emissions, then such a situation will be depicting a high emissions scenario. Emission sce-narios that better fit the SSA context (acknowledging such exceptions as the oil-producing Nigeria or the technologically advanced South Africa) can facilitate better adaptation planning.
Thus the projected changes only show trends and as such are not adequate to identify ecosystems, or regions most vulnerable to climate change.
De-horizons of government organisations remains a concern (Nyong 2005).
While the knowledge from the GCMs is very useful for managing climate issues at global levels (monitoring, change detection, global climate re-gimes) the foregoing indicates the need to refine the resolutions of climate models as GCM-based projections are less useful for adaptation planning at lower social-ecological units like the national, sub-national and commu-nity levels (see for example Wilbanks 2002).
The foregoing suggests that climate projections for SSA do not fit the spatial and temporal scales of agricultural processes, practices or planning and cannot yet produce the details needed for impacts assessments. These limitations of climate change projections need to be understood and ac-counted for in research, policy and planning that depart from or aim to account for climate change impacts. It also shows that "dealing with un-certainty" becomes a major focus for adaptation. The situation analysis in this chapter also highlights that climate change is but one of the many factors that SSA agriculture has to deal with and that adaptation has to account for the multiple stresses facing the sector.
2.5 Conclusion
So where should interventions in smallholder agriculture start? Consider-ing the plethora of challenges facConsider-ing smallholders in SSA and the multiple dynamic stressors (among them climate change), the answer seems to be by learning to deal with change while maintaining the quality of land resources. Considering that many smallholders are poor and have low human capital, one entry point is to increase their knowledge and skills and to encourage those smallholders holding knowledge to share this with others. Considering the progression of land degradation, it is crucial that the development community encourage Sustainable Land Management (SLM) practices as SLM not only improves soil fertility and crop yields, but also increases soil carbon. However, considering that many small-holders are poor, there are limits to their capacity to influence framing conditions – the availability and access to services. Hence, the govern-ments need to improve the provision and quality of services like roads, rural financial services, information dissemination, to mention just a few, and development cooperation should support them. The implementation of the many good policies on paper needs to be evaluated, not only for their
effectiveness, but also for identifying entry points for integrating adapta-tion, where these do not exist already.
The above considerations provide three complementary entry points for adaptation to climate change:
– at a conceptual level through adopting resilience as an analytical lens
– at the local level through examining smallholders’ practices, adaptation and livelihood strategies, and
– at the policy and institutional levels by examining how they support smallholders to adapt through various instruments.
In the following, the conceptual and analytical frameworks are elaborated.