For someone more used to the quiet productivity and relative inconspicuousness of the PhD office at IDS, the headquarters of the UK Meteorological Office in Exeter is impressive and intimidating in equal measure. Mazes of desk separators fill vast open plan offices and obscure a sea of computer screens displaying intriguing and animated maps. It feels very much like the climate modelling hub that it is, as much an industrial factory of climate forecasts as it is a place of intellectual exchange and research. You immediately get a sense of the complexity and scale of the whole operation behind producing climate model projections.
My visit to the Met Office back in January 2012 was the beginning of an exciting year in which I had the opportunity to follow these climate model projections all the way to Kenya, to their use in projects that made predictions of the country’s future yields of maize and ultimately in the design of climate change adaptation interventions for smallholder maize farmers.
It was a fascinating process, not just because of the mystery of the sophisticated computer programmes through which vast data sets gradually became simple pictures of the future world, but also because of the way that understandings and meanings became attached to these pictures.
Undoubtedly what went missing along the journey were the uncertainties, assumptions, and methodological choices that were such a big part of the initial modelling endeavour. By the time it came to promoting technologies designed to help farmers adapt to the growing threat of water shortage, for example, the overwhelming outputs of the Exeter’s weather forecast factory had been reduced a single supposed truth, that ‘climate change will lead to increased drought’.
Of course there is a political motivation captured within this end product, but there is also a politics of knowledge that transcends the whole chain through which it is produced.
In a recent paper published in ‘Climatic Change’, I begin to unpack some of this politics of knowledge, by looking critically at how the growing complexity of climate impact modelling endeavours, and the journey that their outputs go on, are changing the industry of climate impact knowledge and the nature of this knowledge itself.
Exposing this politics is not about fanning the flames of often unreflective climate scepticism, but is rather a call for a more inclusive and transparent process of evidence production and evidence interpretation. I argue that from a more plural ‘evidence-base’, adaptation programmes, policies and interventions might respond to uncertainty and contextual appropriateness rather than to a reductionist and linear understanding of change.
Stephen Whitfield is a PhD candidate within the IDS Knowledge, Technology and Society research team. His research focuses on the construction of a particular ’pro-poor’ climate change adaptation pathway in Kenya, looking at how alternative knowledges, both from within and outside of Kenya, are framing and driving a technology-centred approach to adaptation.