Climate change predictions in Sub-Saharan Africa: impacts and adaptations

Summary

Africa is probably the most vulnerable1 continent to climate change and climate variability. It is also a continent with a diverse range of agro-ecological and geographical features. The impacts of climate change will thus greatly differ across the continent, and even within countries. The Sub-Sahelian Area (SSA) is the world's poorest region with climatic and ecological vulnerability greatly enhanced by the socio-economic situation and the low adaptive capacity. An estimated 90% of the continent's population depends on rain-fed crop production and pastoralism to meet its basic food supplies. Thus across SSA gross domestic product and food production depend directly on climate. As a result of global warming, the climate in Africa is predicted to become more variable, and extreme weather events (like drought, heat waves and flooding) are expected to be more frequent and severe, with increasing risk to health and life. In many regions even small changes in temperature, precipitation and water availability can have devastating effects on agricultural output and therefore on food security. Famine and widespread disruption of socio-economic well-being are possible consequences. Some scenarios indicate that Africa may lose close to 50% of its revenue from agricultural production due to climate change. In many parts of Africa warmer temperatures and changes in amount and seasonality of precipitation will likely destabilise agricultural production that today still depends largely on rainfall for irrigation. Decreasing precipitation and water supply coupled with an increasing population will lead to severe water scarcity and stress. Further food and water shortages and increasing desertification are one plausible scenario that follows from shorter growing seasons and lower yields, and the overall loss of large areas suitable for agriculture or pastures. It is unlikely that areas that possibly will receive more precipitation can provide sufficient compensation. In addition, even today, about one third of water-related disasters of the world ? flooding, landslides and drought ? occur in Africa (UN/ISDR, 2004) and one third of people in SSA already live in drought-prone areas and are vulnerable to the impacts of droughts; moreover floods and droughts can occur in the same area within months of each other. Climate change is likely to intensify already existing detrimental conditions, natural or human induced, like land degradation and desertification, fires and deforestation, loss of wetlands and water stress, declining biodiversity, increasing dust storms, spread of climate sensitive diseases (malaria, tuberculosis, diarrhoea, etc.). Poverty and human migration, together with rapid urbanisation, already alter the environment even further and will continue to do so. This delicate situation will be even worsened by population growth and rapidly changing land-use patterns. All the above statements show that Africa is already a continent under a heavy pressure from climate stresses and due to its current low adaptation capacity, it is highly vulnerable to the impacts of climate change. Therefore stakeholders need the appropriate and most up-to-date tools to better understand and predict climate change, assess its impact on African ecosystems and population, and evaluate and undertake the correct adaptation strategies. In this respect there is an urgent need to i) develop improved climate predictions on seasonal to decadal climatic scales in SSA, ii) evaluate climate change impacts on water and agriculture, iii) improve early warning systems from short to medium-long term predictions and iv) propose new and feasible adaptation strategies, especially fitted for the weakest communities.
Our work will focus on SSA climate predictability and on the improvement of climate prediction models by virtue of developments in their land surface initialization/representation. The improvement of the prediction of climate over SSA is considered from the perspective of both the large-global scale (i.e.: global coupled climate dynamics modelling) and the small-local scale (i.e.: mesoscale and statistical modelling/downscaling). The climate modelling and analysis activity will require as input from the effects of past climate variability, a dataset of SSA surface parameters such as land use and field capacity as well as observationally based historical datasets of soil moisture, vegetation, surface fluxes and meteorological forcing. For sake of consistency, the global scale Coupled CPS will need the same dataset as above but extended to cover the entire globe.
More specifically we will focus on: Assessment of land-climate coupling and feedbacks
Statistical techniques such as the coupled manifold will be used in order to evaluate the feedbacks and the coupling (spatio-temporal) between climate variability/change and Sub-Sahara land surface in both observations and numerical models. Through the availability of new observed datasets, CMCC will assess the coupling and the reciprocal forcing between soil moisture-vegetation, evapotranspiration and rainfall over SSA as a follow up of the recent research described in Alessandri and Navarra (2008). (VUA, CMCC, ICPAC).
Development in mesoscale modelling and regional predictions: Because of sub grid scale feedbacks between land cover, soil moisture and rainfall, part of the predictability of seasonal climate in SSA depends on scales smaller than resolved by the GCM. The BRAMS (Brazilian Regional Atmospheric Modelling System) will be further developed in order to include a phenology description. The BRAMS model will be used to simulate the regional meteorology of the Sahel and Southern Africa region at a spatial scale of 0.25 degrees and a temporal scale of days/weeks to assess the role of feedbacks. We will use surface flux observation to calibrate and validate the sophisticated land surface part of the model SiB3. The sensitivity of the prediction skill to the interactive phenology scheme model versus a prescribed climatological phenology will be evaluated. A series of simulation experiments will be carried out considering permutations of soil moisture, LAI, fAPAR and Albedo alternatively prescribed by Earth Observation data (WP1) or calculated interactively within the Land surface model. (VUA).

Details

Project number

SH-039-09-V

Main applicant

Prof. dr. A.J. Dolman

Affiliated with

Vrije Universiteit Amsterdam, Faculteit der Aard- en Levenswetenschappen, Earth Sciences

Duration

18/12/2009 to 14/08/2012