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).
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:
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.
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.
AGENT-BASED SOCIAL SIMULATION
The three elements of the project are brought together with a consistent user interface for easy access. Outputs include
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.
DETAILED CLIMATE OUTLOOKS
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.
CROP AND WATER RESOURCE IMPACT MODELLING
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
School of Geography and the Environment
Oxford, OX1 3TB, UK
School of Geography and the Environment
Oxford, OX1 3TB, UK
Thomas E. Downing
Stockholm Environment Institute, Oxford Office
10B Littlegate StreetOxford OX1 1QT, UK
Department of Geography
Cambridge CB2 3EN
The Tyndall Centre for Climate Change Research
School of Environmental SciencesUniversity of East AngliaNorwich, Norfolk, NR4 7TJ, UK