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T2.32: Climate Outlooks and Agent-Based Simulation of Adaptation in Africa.

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T2.32: Climate Outlooks and Agent-Based Simulation of Adaptation in Africa

Richard Washington (School of Geography and the Environment, University of Oxford), Mark New (SoGE), Thomas E. Downing (Stockholm Environment Institute), Mike Bithell (Department of Geography, Cambridge University), Alex Haxeltine (Tyndall Centre for Climate Change Research)

Bruce Hewitson (University of Cape Town), Chris Reason (UCT), Roland Schulze (Natal University), Coleen Vogel (University of Witwatersrand), Emma Archer (IRI and UCT), Edmund Chattoe (Department of Sociology, University of Oxford), Gina Ziervogel, SoGE Oxford and SEI), Sukaina Bharwani (SEI) Matt Swann (SoGE Oxford)

Agent Based Social Simulation

Fieldwork in Southern Africa


We approach climate change adaptation as a learning process, in that the development of adaptive capacity to climate forecasts on shorter time-scales enhances adaptive capacity on all time-scales, extending from seasonal and interannual to decadal and beyond. Climate change and climate variability are closely linked in the operation of the climate system. Some of the largest impacts of climate change may arise through the superposition of more intense forms of existing modes of variability on an underlying global warming trend.

It is proposed that adaptation strategies geared to cope with large climate anomalies would embrace a large proportion of the envelope of change expected from long term climate change. This idea has become ‘conventional wisdom’, endorsed, for example by WMO CLIPS and the World Bank. However, it has not been tested in a rigorous manner.

This project will develop an innovative modelling framework that integrates social responses to climate events and climate predictions on a continuum of time-scales, thereby enabling the exploration of adaptation as a learning process.

The methodology will be applied using a southern African case study because Africa is arguably the continent most vulnerable to climate change, and southern Africa is an area where seasonal climate prediction is already operational (Washington and Downing, 1999).

  • In previous research:
  • Stakeholders were interviewed on their use of climate information
  • Potential impacts of climate change on agriculture, water and vulnerability of small farmers was documented
  • The case study was selected: Mangondi village in Limpopo Province

See Archer, E. 2002 Identifying Underserved End-User Groups in the Provision of Climate Information, Bulletin of the American Meteorological Society (Submitted).


The team has built a prototype model that will address:

    • Ways in which information is passed between model elements, including farmers, chiefs, extension agents and meteorological offices
    • Design of the user interface
    • Aspects of the core model
  • Outputs include figures 1 and 2 (right), detailing the possible effect of forecast implementation on costs to farmers (figure 1) and levels of trust in the forecasts (figure 2).
  • The prototype model is being extended, and is currently being developed to include a more sophisticated crop model, more detailed climate inputs, and further economic and demographic components.

The potential impact of forecast adoption on a community for which rainfall fluctuations may lead to regular food shortages has been modelled. The figure shows the cost (in tonnes of grain) that a farmer would have to bear as a result of years where stored food levels drop to zero. Plotted are mean cumulative costs as a function of forecast skill, with standard error generated from 500 simulated climate sequences, for a household of eight people farming a single hectare field for 50 years. Typical normal grain yield for this region is 1 tonne per hectare. The dotted horizontal line and blue cross show the no forecast case. In red is the case where the incorrect forecasts are always as poor as possible, and in green those where forecast is incorrect by two terciles at most 10% of the time. Early results suggest that where the rainfall does not fall in the forecast tercile more than 60% of the time, farmers may be worse off using the forecast than ignoring it.

  • Requirements from fieldwork:
  • Construction of adaptation-decision making scenarios. The suite of socio-economic futures constructed for greenhouse gas emissions (the SRES scenarios) will be supplemented by southern African development scenarios
  • Map of social networks among farmers that govern dissemination of information
  • Objectives:
    • Translate stream of climate outlooks into forecasts
    • Trace dissemination and use of forecasts
    • Model decision-making among smallholder farmers
  • Design based on:
    • Prototype
    • Review by project team and additional experts
    • Discussions with local stakeholders
  • Model used in two modes:
    • Optimum forecast mode, using ‘perfect’ forecasts and responses
    • ‘Realist’ mode degrades optimum chain, with imperfect, probabilistic forecasts. Decision-making reflects social processes and perception

Growth of stakeholder trust when poor forecasts damage the trust level. As the number of failed wet year forecasts increases, the mean level of trust starts to decline. Note the long timescale over which this takes place. The curves shown are means over 500 climate sequences. Scatter about the mean is of the same order as the mean itself.

Clockwise from above: mixed crops/fallow in Mangondi village; Emma Archer, Gina Ziervogel and Tom Downing are shown maize crops by a local farmer; Gina Ziervogel and a local farmer.

Climate Outlooks


  • Development of streams of climate data that provide probabilistic climate outlooks on three time scales
    • Interannual (associated with seasonal forecasting)
    • Multi-decadal
    • Long-term climate change
  • The streams will drive resource models and agent-based simulation on the three time scales
  • A range of model integrations will be used, including UKMO, incorporating both dynamic and empirical downscaling of ensemble model runs
  • Multi-decadal variability and climate change (2099) timescales will be sourced from the IPCC data distribution centre and the LINK Project at the University of East Anglia Climatic Research Unit.

The three elements of the project are brought together with a consistent user interface for easy access. Outputs include

  • Time series of climate outlooks
  • Characteristics of adaptation pathway scenarios
  • A vulnerability profile
  • Indicators such as the water poverty index being developed for DFID by the University of Natal.
  • The integrated framework of climate outlooks, ABSM and a user interface will be reviewed and assessed in South Africa
  • The assessment will include:
    • Participatory exercise in the selected village
    • Workshop within the regional SARCOF meeting to show the approach and elicit comments from experts
    • Extended training and demonstration within Wits University to discuss ways to take the methodology forward.

These figures show the mean correlations between JFM Nino3 and southern African JFM rainfall for active (left) and inactive (right) 30 year ENSO periods. The 30 year periods are obtained from all the climate change runs of HadCM3 used in the study, and the correlation coefficients for active and inactive periods are averaged at each gridbox. There are six active and nine inactive periods. During active ENSO periods (similar to the last 30 years in the observed record), El Nino events are associated with negative rainfall anomalies in southern Africa, but this link weakens when ENSO is less active (comparable to 1941-70 in the observed record). The implications for seasonal forecasting and decadal-scale planning are significant. El Nino is not always as powerful a predictor of southern African rainfall as it is currently. This highlights the need for adaptation strategies on a continuum of timescales.


Summer sea surface temperature correlations with Mangondi region rainfall, from the Hadley Centre climate model (HadCM3), forced with greenhouse gas increases of 1% per annum.



Archer, E.R.M. 2002 Identifying Underserved End User Groups in the Provision of Climate

Information: Bulletin of the American Meteorological Society (submitted)

Washington, R. and Downing, T.E. 1999: Seasonal Forecasting of African Rainfall:

Geographical Journal, 165 pp255-274

  • The principal impacts of climatic variations for smallholder farmers are effects on agriculture and water resources
  • The FAO model ‘CropWat’ has been adapted to run together with the agent-based simulation, providing information on a variety of crops

Richard Washington

School of Geography and the Environment

Mansfield Road

Oxford, OX1 3TB, UK

Mark New

School of Geography and the Environment

Mansfield Road

Oxford, OX1 3TB, UK

Thomas E. Downing

Stockholm Environment Institute, Oxford Office

10B Littlegate StreetOxford OX1 1QT, UK

Mike Bithell

Department of Geography

Downing Place

Cambridge CB2 3EN


Alex Haxeltine

The Tyndall Centre for Climate Change Research

School of Environmental SciencesUniversity of East AngliaNorwich, Norfolk, NR4 7TJ, UK