notes from the field

These field notes are personal views and do not necessarily reflect the views of Red Cross/Red Crescent Climate Centre

Kanmani Venkateswaran

Lusaka, Zambia
May 11 – August 11

May 16, 2013

I have just arrived in Lusaka, Zambia for my work with the Zambian Red Cross. Lusaka has been wonderful so far. Even before I stepped out of the airport, I had built a support network of Zambians I had met during my journey from Boulder to Lusaka. Upon leaving the airport, I was greeted by Joanna from the Zambian Red Cross. She drove me from the airport to my friend’s house, all the while patiently navigating my barrage of excited questions about Lusaka. Looking out of the car window, I couldn’t help but notice how much parts of Lusaka looked and felt like parts of South India – the dusty roads, the hot and dry air, small businesses lining the sides of the roads, music blasting from corners, children walking to school, and women washing clothes outside their houses. I am so excited to start working with the Zambian Red Cross, learn more about Lusaka, and explore more of Zambia.

While in Zambia, I will be working with the Red Cross on linking seasonal climate forecasts with humanitarian decision-making. The expectation is that seasonal climate forecasts will allow scientists and policymakers to anticipate climate hazards and disasters and accordingly implement measures to secure livelihoods and overall improve adaptive capacities of vulnerable communities (Lemos and Dilling 2007). Climate forecasts, however, have not been used to their full potential and in ways that ensure greater welfare, livelihood security, and equity (Lemos and Dilling 2007; Vogel and O’Brien 2006). This is, to a large degree, because climate forecasts have not been tailored to the local context. This is apparent in the process of forecast production, dissemination and use.

There are two ways in which climate forecasts are commonly produced: the Science Push and the Demand Pull (Dilling and Lemos 2011). The Science Push is where knowledge production is driven by the “pursuit of knowledge”, rather than the need for solutions, and when scientists assume what information decision-makers need. The problem with this is that forecast users may perceive their needs differently from scientists, thereby limiting the usability of the forecasts (Lemos and Rood 2010). The Demand Pull is where knowledge users determine what information they need from scientists. In this situation, the knowledge demanded can be difficult to produce given the limitations of climate models and scientists’ knowledge (Sarewitz and Pielke 2007). Users need to understand the limitations of the forecasts. A further problem with both the Science Push and Demand Pull methods is that they do not take into consideration the range of perceptions of need and information usability, based on values, beliefs, and lived experiences, that exist within knowledge producer and user communities (Lemos and Rood 2010; Adger et al 2009). Climate forecasts need to be co-produced by forecast users and producers (Dilling and Lemos 2011).

The dissemination of climate forecasts has also been problematic. Forecasts are commonly presented as probabilistic models and scientists have the tendency to communicate using technical jargon. Such complex and precise forecasts make it difficult for humanitarian aid organizations to decide whether or not to evacuate, ready relief items, mobilize disaster response teams, and/or ask for donor support (Suarez and Tall 2010). Forecasts, therefore, need to be communicated in ways that users can understand. The mode of forecast dissemination has also been problematic. Many forecast-producing institutes disseminate their forecasts on platforms such as the Internet which poorer, vulnerable communities do not necessarily have access to. An effort to disseminate forecasts to vulnerable communities and engage public participation in understanding what the forecasts mean will allow knowledge producers and decision-makers to tailor forecasts and decisions, respectively, to the local context (Roncoli et al 2009; Robbins et al 2010).

The use of climate forecasts is largely dependent on their production and dissemination. Forecasts will not be used if users do not think they are credible. Credibility is determined by the outcome of previous forecasts and user trust of the knowledge producer and/or communicator. In addition, forecasts will either not be used or will be used incorrectly if the forecast is communicated in inaccessible, difficult, and confusing language. Forecasts are also not necessarily used when the spatial and/or temporal scales of the forecasts are too large to be downscaled. Moreover, issues of forecast legitimacy arise when users question the political motive of the knowledge producer and/or communicator. Does the forecast recommend a behavioural change for one user group that would disproportionately benefit another user group? Does the forecast clash with traditional climate indicators and modes of climate production? In situations where forecast legitimacy is contested, users will ignore the forecast and any related advice (Patt and Gwata 2002).

Decision-making and implementation tend to be isolated from the local and social contexts. The vulnerability of a group is often determined by its relative location to a particular climate hazard (Suarez and Tall 2010). Vulnerability, however, is also influenced by power, agency, socio-economic factors, and so on. A relatively wealthy and politically connected community located in the direct path of a climate hazard may be less vulnerable than a poor and marginalized community located at its fringes. The resulting loss expected from a disaster and the range of plausible actions that can be taken before, during, and after a disaster are highly dependent on this vulnerability. In addition, decision-makers need to understand what the forecasts mean in the local context before they can make effective forecast-based decisions. For example, what does 500 mm of rain in this particular area mean given the topography and culture and livelihoods of the local people?

Overall, the process of climate forecast production, dissemination and use is too linear and divorced from its broader and complex social context (Vogel and O’Brien 2006).  Consequently, my goal while in Zambia is to reduce the gap existing between scientists, humanitarian aid workers, and local communities by understanding what climate forecasts mean in the local context and generating a set of recommendations on actions to take in the event of climate hazards as a means to minimize losses. These recommendations will be constructed by bringing together local and scientific knowledges.

References:

Adger, W. N., Dessai, S., Goulden, M., Hulme, M., Lorenzoni, I., Nelson, D. R., ... & Wreford, A. (2009). Are there social limits to adaptation to climate change? Climatic change, 93(3-4), 335-354.

Dilling, L., & Lemos, M. C. (2011). Creating usable science: Opportunities and constraints for climate knowledge use and their implications for science policy. Global Environmental Change, 21(2), 680-689.

Lemos, M. C., & Dilling, L. (2007). Equity in forecasting climate: Can science save the world's poor?. Science and Public Policy, 34(2), 109-116.

Lemos, M. C., & Rood, R. B. (2010). Climate projections and their impact on policy and practice. Wiley Interdisciplinary Reviews: Climate Change, 1(5), 670-682.

Patt A.G. and Gwata, C. (2002). Effective seasonal climate forecast applications: examining constraints for subsistence farmers in Zimbabwe. Global Environmental Change - Human and Policy Dimensions 12(3): 185–195.

Robbins, P., Hintz, J., & Moore, S. A. (2010). Environment and society: a critical introduction. Environment and Society: A Critical Introduction. Wiley-Blackwell, Hoboken, NJ.

Roncoli, C., Jost, C., Kirshen, P., Sanon, M., Ingram, K. T., Woodin, M., ... & Hoogenboom, G. (2009). From accessing to assessing forecasts: an end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Climatic Change, 92(3-4), 433-460.

Sarewitz, D., & Pielke Jr, R. A. (2007). The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy, 10(1), 5-16.

Suarez, P., & Tall, A. (2010). Towards forecast-based humanitarian decisions: Climate science to get from early warning to early action. HFP.

Vogel, C., & O Brien, K. (2006). Who can eat information? Examining the effectiveness of seasonal climate forecasts and regional climate-risk management strategies. Climate Research, 33(1), 111.