Climate science, and the humanitarian system: A case for process over product

By Dominic Kniveton (University of Sussex)1 with contributions from Lewis Sida (University of Sussex); Emma

Visman (HFP); Marieke Hounjet (Consortium of British Humanitarian Agencies); Benedict Dempsey (Save the Children); Charlie Mclaren (UKCDS);

Summary As the international humanitarian system attempts to move from its focus on relief to being more anticipatory and resilient to the stress multipliers of climate change and variability, so its engagement with scientists on the subjects of uncertainty and risk becomes more important. Evaluations of the response to the Horn of Africa crisis in 2011 have highlighted the ill-ease of humanitarian based institutions to act upon forecasts that are inherently uncertain due to the fear of the impact on their finances and reputation if the forecasts fail to be accurate; of being seen as too interventionist, and so undermining the local community’s own coping capacity if they become involved before crises are fully in flow; and of risking donor fatigue if the number of forecasts requiring action increases. In contrast, local communities appear more open to the concept of uncertainty having based their livelihoods on decisions made with incomplete knowledge and information in dynamic circumstances. Uncertainty lies at the heart of much of climate science, from its role in generating a rationale for research, to its position as a fundamental characteristic of predictions of the state of the atmosphere over time. However climate science has an awkward relationship with communicating scientific uncertainty to non scientists. In a parallel with humanitarian decision makers, scientists also fear the impact of not being fully deterministic on their potential to attract research funding and on their scientific reputation; that if uncertainty is acknowledged this will dilute any willingness to act by policy makers and also that the wider public will not understand probabilistic information. It is argued in this paper that by engaging in a process of dialogue and collaboration the humanitarian institutions, local communities and climate scientists can develop the understanding and knowledge to facilitate the changes required of the humanitarian system. In particular the case is made for encouraging pluralism and linkages across multiple actors, communication and negotiation between the different epistemological communities; by promoting decision making whereby policy/practice decisions are regarded as flexible and adaptive rather than definitive solutions; and that rather than focusing on certainties making an explicit recognition of uncertainties and an emphasis on social learning and the co-evolution of knowledge. In this context the example is given of the potential to develop a framework by which information on the variation over time and space of the levels of uncertainty in seasonal forecasting may contribute to the development of a set of policy and practice pathways for humanitarian action that are flexible and adaptable to changing circumstances.

1 For correspondence please contact: Prof. Dominic Kniveton, School of Global; Studies, University of Sussex,

Falmer Brighton. E-mail:kafw3@sussex.ac.uk

Introduction Variously described as a gap or bridge, the nexus of science, policy and practice is a pertinent feature of concern in a variety of arenas involving the major global issues of today including humanitarian response and development. Despite considerable progress in developing new approaches, tools, methodologies and models to understand and forecast environmentally related events there remains a tangible lack of uptake of this knowledge to prompt action by policy makers and decision takers. For example the 2011 crisis in the horn of Africa was forecast as early as mid 2010 with predictions of an increased probability that the October to December rains would be below average (FEWSNET 2010a, b). Yet the full-scale response from the national and international communities did not occur until after the rains had failed for a second successive and predicted time and people were already suffering and dying (Save the Children Fund and Oxfam 2012). While it is recognised that prospective users of science do not regularly read scientific journals and academic books, nor that scientific outreach often fails to go beyond transmitting the products of research to websites and media outlets, necessitating a range of intermediaries to interpret and reframe the results of science to decision–makers (Moser and Dilling 2007), the problems of the science-policy-practice gap go further than these relatively straightforward reasons would suggest. In this paper we attempt to explore some of the reasons for this with reference to the humanitarian system. In particular the paper will explore the different spheres that science, policy and practice occupy and identify the role that uncertainty plays in each sphere. Furthermore the framing of the interaction of the science, policy and practice communities is often such that it is biased towards a one way exchange in which knowledge is communicated (or learning received) from scientists to users rather than developed within a process of interaction between the different epistemological communities. In recognition of these characteristics of interaction we attempt to outline a framework or shared space within which scientists, policy makers and practitioners can come together to develop new knowledge and understandings to support appropriate decision-making. Theoretical framework The framework chosen borrows from the theoretical standpoint of Complex Adaptive Systems (CAS) from the discipline of ecological economics. Complex Adaptive Systems can be described as systems involving ‘nested hierarchies, a multiplicity of cross-scale interactions and feedback loops between different hierarchical levels characterised by a high degree of complexity and non-linear behaviour that predictive equilibrium models fail to calculate’ (Van den Bergh and Gowdy, 2003, Rammel et al 2007). Previously this concept has been applied to social-ecological systems in such applications as environmental management and migration and climate studies (Rammel et al 2007, Kniveton et al 2012). In this paper we instead apply the concept of CAS to understand the non-bounded system as defined by the interaction of science, policy and practice in the field of humanitarian response. Both science and humanitarian policy and practice operate in a variety of hierarchical and nested levels of social, cultural, economic, and political contexts as well as being connected to each other with feedbacks such as practice outcomes influencing research and funding objectives, as well as generating data for research. For climate science, research methods are often aligned along disciplinary lines from social science to physics with research directions influenced by previous experience of research outcomes and funding streams. Whilst cross-

and inter- disciplinary research exists the nature of training of researchers in largely subject based university departments remains an impediment to the development of individual research thinking from multiple perspectives. Behaviour in practice can also be dependent on heuristics derived from previous experience of outcomes and is set within socio-cultural, political and economic boundaries. The resulting complexity of this interaction between science- policy and practice has been noted to produce both non-linear and emergent outcomes, with changes sometimes occurring suddenly, but in other circumstances failing to do so despite seeming evidence of effect (New Humanitarian Agendas: Forging a policy-relevant research agenda workshop, University of Sussex, 19 Oct 2011). In terms of innovation this can result in behavioural change that can often appear irreducible and unpredictable to traditional methods of analysis. The focus on uncertainties is particularly apt within climate science due to the chaotic nature of the atmosphere resulting in large natural variability and the unknown levels of future radiatively active gases constraining longer term predictability. While for the humanitarian community uncertainty arises from the growing complexity of crises and the recognition that the humanitarian response needs to move to a more anticipatory approach from a reactive one. Furthermore the impacts of climate on society operate within nested levels with the frequency of shocks such as floods and droughts having the potential to overwhelm development programmes and practice communities trying to adapt to more gradual trends of climate change. The humanitarian system sphere According to the joint Agency briefing paper from Save the Children Fund and Oxfam (hereinafter referred to as ‘A Dangerous Delay’), at first glance, the reason that the international system was so slow to respond to the early warnings of the horn of Africa crisis in 2011 was because fund raising depends on significant media and public attention, which in turn is reliant on evidence of an existing crisis. However more fundamentally it suggests that the real cause for the delay lies within the humanitarian system, and with decision-maker ill ease at acting upon forecasts that are inherently uncertain. Delving further into these institutional reasons it posits that in acting on uncertain forecasts, humanitarian based institutions may fear the impact on their finances and reputation if the forecasts fail to be accurate; of being seen as too interventionist, and so undermining community’s own coping capacity if they become involved before crises are fully in flow; and of risking donor fatigue if the number of forecasts requiring action increases. Additionally it may even be argued that, in a context of funding dependent on evidence of impact, showing that a crisis has been averted is more difficult than showing that a crisis has been alleviated and thus preventative efforts may be relegated to a lesser priority. To understand how these reasons have traction with institutions it is worth understanding how the international humanitarian system broadly works at present. Overall the formal international humanitarian system has been constructed as comprising providers, such as donor governments, foundations and individual givers and delivering agencies including the Red Cross and Red Crescent Movement, NGOs, and UN Agencies. In practice the most important element of this system includes those directly affected by crises, not only as victims but also as implementers of their own response, including national governments, local NGOs and communities themselves. For international humanitarian

actors, an ongoing challenge is how to complement the existing resilience and coping mechanisms of local communities, who are always the first to respond to disasters. As the ultimate practitioners of anticipatory behaviour to avert humanitarian crises, local communities have an intimate relationship with uncertainty and risk in virtually all of the decisions that are made (although rarely using such terminology). For example the decisions on which crops to plant, and when, are often as much a function of the uncertain conditions of seed, fertilizers, and labour availability, and local market conditions as they are of any local or scientifically based information of potential atmospheric and climatic conditions. However even these factors are often inadequate in explaining decisions taken by individuals and households at risk of environmental hazards. Extensive studies from social psychology have also highlighted the importance of the socio-cognitive variables of attitudes towards a particular behaviour, subjective norms and perceived behavioural control in decision-making for a variety of planned behaviours (Ajzen 1991). In particular the role of the wider social discourse can be seen on the assessment of risk and in its impact on the perceived acceptability to engage or not to engage in a behaviour or with information such as seasonal forecasts. Recent work on the uptake of agricultural innovation by farmers in an Australian context has also shown the importance of the locus of control on taking risk by decision takers (Price and Leviston, in press). In this context the locus of control is taken as the extent to which individuals believe that they can control events that affect them. Any understanding of how scientifically based climate information permeates through to decisions made at the ground level needs to consider how attitude, perception, social discourse and subjective norms interact with the concept of scientific uncertainty. Furthermore the issues of trust and credibility play important roles in the ways in which uncertainty in climate information is dealt with. Emerging evidence from the Humanitarian Futures Programme-led climate scientist–humanitarian policy exchange has highlighted the role that local climate knowledge plays in decisions taken by communities. In particular it has revealed the community acceptance of uncertainty within this information and thus presented a potential pathway to the integration of uncertainty in scientifically derived climate information into decisions. In terms of the international humanitarian system, responses to crises are initiated when there is an appeal for international assistance by a national government or directed by the UN. However according to A Dangerous Delay, “national governments often see an emergency declaration as a sign of weakness, especially if there is a drive for food self-security. This can make it difficult for humanitarian agencies to declare an emergency themselves.” (Save the Children Fund and Oxfam 2012: 14). Thus the declaration of emergency operates in contested space with humanitarian agencies in some cases viewing emergencies as an opportunity to fundraise, and national governments being wary of potentially false declarations for this very reason. Furthermore declaring emergency can knock investor confidence and the economy can take a hit as a result, hence governments from Bangladesh to Ethiopia are increasingly wary of the ‘E’ word. In terms of food insecurity an important indicator of a serious emergency is Phase 4 of the Integrated Phase Classification (IPC)2 . Developed by the FAO Food Security Analysis Unit the classification combines indicators of ( 1) contributing factors such as, hazards & vulnerability, food availability, access, utilization, & stability; human water requirement from improved source; 2 http://www.fews.net/docs/Special/Acute-Food-Insecurity-Draft.pdf

(2) household outcomes such as food consumption, livelihood change, nutrition, mortality; and (3) the area contextual outcomes of nutrition and death rate. In this classification the physical determinant of the environment, as expressed through hazards, is phrased as having occurred rather than predicted, in all phases of the IPC. Whilst the humanitarian responses of critically urgent protection of human lives and provision of comprehensive assistance with basic needs (e.g. food, water, shelter, sanitation, health, etc.) is only suggested as being activated with the presence of severely acutely malnourished child and/or mothers and a significant increase in mortality. The need for the humanitarian response to move to that based on anticipation has been articulated by both A Dangerous Delay and DFID’s Humanitarian Emergency Response Review (2011) with suggestions of a reorientation towards risk, rather than crisis, management and the longer term promotion of resilient households, communities, and nations. Here, humanitarian response overlaps significantly with well-established approaches including Disaster Risk Reduction and Management (DRR/DRM), local capacity building, Climate Change Adaptation (CCA), integrated development and humanitarian programming, and building resilience. The concept of resilience is increasingly being used in a variety of fields from psychology, structural engineering, social-ecological systems and corporate strategy. Whilst differently characterised according to the field of use, resilient systems have at their core a capacity to anticipate and manage both known and unknown risks and challenges requiring flexibility which in turn can be achieved through pursuing a strategy of diversification and preparedness (Interagency Resilience Working Group 2012) while recognising the non-equilibrium nature of the system (see Scoones 2004 for developmental perspective). Clearly given the above context the humanitarian system should not only concentrate on the promotion of resilient households, communities and nations as recipients of humanitarian action but could well do with a reorientation of itself to a resilient one. Within this context the suggestions of A Dangerous Delay, including to: embed a risk reduction development approach within the international aid community; allow long term development interventions to adapt to a changing context; undertake preventative humanitarian work on the basis of forecasts; review organisational structures to integrate risk management throughout the development and humanitarian cycle; provide a more agile and flexible funding mechanism, can all be seen to help create a more resilient international humanitarian system. In terms of the interaction with science the movement to risk management requires the development of agreed triggers for early action. In the next section we explore the scientific sphere in relation to the physical climate to explore the role that uncertainty plays in the generation of knowledge. The climate science sphere Uncertainty lies at the heart of much of climate science, from its role in generating a rationale for research, to its position as a fundamental characteristic of predictions of the state of the atmosphere over time. The seminal work of Edward Lorenz embedded the theory of chaos at the centre of meteorology. He showed that even for a simple set of atmosphere describing non-linear equations, that the evolution of the atmosphere could be changed by minute perturbations to the initial conditions of a simulation i.e. when the forecast was made. These perturbations are so small that realistically no system of observations could ever specify them. Thus in a stroke he showed that deterministic forecasts of the future state of the atmosphere were impossible to achieve and that all

forecasts must be treated as probabilistic. Yet, through the examination of the trajectories of different projections of the atmosphere with slightly different starting conditions and the discovery of Lorenz attractors, he also found that non-linear systems like the atmosphere may exhibit regime-like structures that although deterministic are subject to abrupt change. Intriguingly however, through investigating such behaviour Lorenz found that the predictability of the atmosphere was flow dependent such that while some weather patterns or regimes may be unpredictable others may contain some predictability and that the level of predictability was itself predictable. In other words Lorenz showed that while the exact state of the atmosphere was impossible to forecast precisely, some general patterns of the atmosphere had some predictability and that the level of confidence in the probability distribution of these forecasts could be assessed (Slingo and Palmer 2010). Conventionally probabilistic forecasts with dynamical models (e.g. those based around non-linear equations) are created by running a number of model forecast runs with small random perturbations applied to the initial conditions of the simulation. The chaotic nature of the atmosphere then amplifies each perturbation leading to a series of divergent forecasts which can be treated as envelopes of possible future atmospheric states. While this technique of creating ensembles of forecasts samples some of the probability distribution of the future atmosphere it soon became apparent that the total distribution of possible states of the atmosphere are underestimated due to systematic errors in individual models. Multi-model runs attempt to sample these errors but inevitably again provide only a select sample of the total uncertainty. Importantly as forecast time is extended the envelope of possible future states expands. Also, the uncertainty in a forecast increases more quickly for smaller spatial scaled phenomena, such as thunderstorms, than for larger-scale phenomena, such as winter storms (Hirschberg and Abrams, 2011). The reason why seasonal and longer time scale forecasts are possible is the relationship of the atmosphere with other slower evolving parts of the climate system such as the ocean. These forcings on the atmosphere allow predictions of the time spent in particular regimes of the atmosphere (or attractors) to be made and thus some predictability of the probability distribution of possible climate states in the future (Slingo and Palmer 2010). So far however we have talked only about forecasts of climate with dynamical models. Currently these models only form a small component of the model base used to form seasonal forecasts in the regional climate outlook forums that take place throughout sub-Saharan Africa. In these forums use is also made of statistical models that essentially are formed from regression analyses of sea surface temperature anomalies and variables such as rainfall. As such these models sample the probability of possible states of the atmosphere through the ability to statistically relate independent and dependent variables. In doing so it could be argued that they do not sample the total probability distribution of the forecast but instead make an estimate of the statistical error of the determination of the relationship between variables. Despite these advances in the understanding of uncertainty3 in forecasting, climate science often has an awkward relationship with communicating scientific uncertainty to non

3 Where the meaning of “uncertainty” to the climate community is taken from Hirschberg and Abrams (2011) as:

“a general, overarching term referring to ambiguities, indeterminateness, or lack of exactness in forecasts. The

cumulative result of uncertainty in a forecast is forecast error—the actual difference between what was

scientists. In a parallel with humanitarian decision makers, scientists also fear the impact of not being fully deterministic on their potential to attract research funding and on their scientific reputation; that if uncertainty is acknowledged this will dilute any willingness to act by policy makers and also that the wider public will not understand probabilistic prediction. Furthermore in terms of climate change the highly politicised space of climate scepticism and alarmists makes the honest assessment of scientific capabilities a difficult path to follow. While these fears may be unfounded in some circles of policy makers and practitioners, the scientific method and academic publishing process, through falsification and the publication of only original scientific findings in the most credible of scientific journals, respectively, can create an environment where it appears that there is more uncertainty in climate science to non specialist audiences than exists to the climatic community. Although contrastingly it has been argued that a certainty trough exists in relationship to levels of uncertainty and involvement in knowledge production with uncertainty higher in those directly involved in forecasting than policy users of model outputs (Mackenzie 1990, Demeritt et al 2010). Furthermore the complex nature of many weather-prediction and climate models developed over decades through the combination of numerous highly specialised process based modules developed by different personnel, means that to all but the very few, the intricate workings of these models remains a black box of uncertainty to many even within the climate community. Process rather product Complex adaptive systems are based on “complex behaviour that emerges as a result of interactions among system components and among system components and the environment. Through interacting with and learning from its environment, a complex adaptive system modifies its behaviour to adapt to changes in its environment“ (Potgieter and Bishop, 2001: 1) The descriptions in the main section of this paper, of some of the characteristics of the spheres in which the humanitarian system and climate science operate provides a window into different perspectives on uncertainty in climate information by climate scientists and the humanitarian system. Clearly it can be seen that each community of knowledge is subject to a variety of pressures and drivers that fall outside the immediate interaction of science–policy-practice but that influence the ways in which information is exchanged and new knowledge is developed between the communities. This shared decision space is depicted in Figure 1. Recent assessments of the Horn of Africa crisis in 2011 have highlighted the need for the humanitarian community to move from a crisis to a risk management approach and thus become more anticipatory in its actions. While it is recognised that the precise nature of many environmental hazards cannot be predicted there exists considerable scientific knowledge of the likelihood of drought and flood conditions occurring at short to seasonal timescales, with on-going work exploring the skill in decadal forecasting. Although the dependence of the future climate on choices made by society, e.g. on levels of emissions of

forecasted to occur prior to an event (e.g., the amount of snow two days from now) and what actually occurred

(was observed) in nature (e.g., the measured amount of snow that actually fell).” (AMS 2011: 1)

greenhouse gases, precludes prediction of future changes at longer time scales, climate models allow the depiction of different scenarios that can be used to plan adaptation strategies. At the centre of the move to a risk management approach is the question of whether enough is known of the uncertainty in early warnings to develop triggers for early action. Some of this uncertainty can be attributed to the climate, but non climatic processes also play a very important role in determining whether a disaster unfolds. With respect to climate science the question then is whether the probability distribution of forecasts of future states of the atmosphere are described with enough accuracy to be used in risk management and under what conditions this predictability of probability is sufficiently accurate to initiate action. Answering these questions requires interaction between science and humanitarian communities. Unfortunately in the past such a process has been largely lacking or unsuccessful. However in framing the interaction of climate science and the humanitarian system as a complex adaptive system we can draw on theories of adaptive co-management to strengthen links between science, policy and practice and thus promote the uptake of knowledge and promote innovation. In this context the emphasis should be placed on promoting collaboration between the epistemic communities through pluralism and linkages across multiple actors, communication and negotiation between the different communities; by promoting transactive decision making4 whereby policy/practice decisions are regarded as flexible and adaptive rather than definitive solutions5; and rather than focusing on certainties making an explicit recognition of uncertainties and an emphasis on social learning and the co-evolution of knowledge (Plummer and Armitage 2007). In actioning this framework the focus should be firmly placed on the process of interaction and dialogue rather than on delivering set products, which instead should emerge from this process. Despite this focus on process the overall aim remains to develop new knowledge on how climate science can be used to support appropriate decision-making by the humanitarian system. In this context it may be possible with further research to first delineate regions with similar patterns of change in uncertainty over time in terms of the ability to provide seasonal forecasts and then to characterise the changes in the probabilities of occurrence, and accuracy or uncertainty of the forecast (see Figure 2 and 3 for hypothetical case) . From a climate perspective, for instance, regions that are teleconnected6 to El Nino Southern Oscillation (ENSO) in the same way, such as the south east of Africa, might also be expected to have the same uncertainty profile over time in the accuracy of the seasonal forecast for La Nina (cold water) events in the Pacific. The representations of the variability in uncertainty in

4 “The essence of the transactive mode is strategy making based on in-teraction and learning rather than the

execution of a predetermined plan (Fiol & Lyles, 1985).” Hart 1992: 338 5 E.g. in developing policies to seasonal forecasts taking into account the probability distribution of the forecast

and pursuing policies that have the ability to change as the situation evolves 6 Where teleconnections are linkages between climate anomalies in one part of the world to another. Changes in

coupled ocean-atmosphere ENSO phenonmena in the Pacific ocean are typically linked to rainfall over many

parts of Africa and these linkages provide the basis for a number of statistical seasonal forecasting systems

developed by national meteorological services and used in the seasonal climate outlook forums.

time and space could provide input into developing different pathways to action by humanitarian organisations. Responses to potential crises could also be tailored to the different realities of assistance required and realistically deliverable as well as the uncertainties present in the climate information provided. Thus different actions could be developed for different antecedent conditions both in the physical environment in terms of soil moisture and in terms of the vulnerability and capacity7 of the communities and humanitarian organisations involved. The example of the 2012 seasonal forecast for the horn of Africa provides some insight into this, with anecdotally humanitarian organisations more likely to prepare for emergency actions with a forecast of a probability of 30% of reduced rainfall because of the crisis in 2011 than they would have been in 2010 if a similar probability had been given. An alternative phrasing of this uncertainty and potential area of research for climate scientists might be along the lines of trying to produce climate information that identifies when a particular percentage of accuracy is achieved of a particular range or level of rainfall. So for example the question arises whether seasonal forecasting could be re-orientated from being delivered at a set time to an alert when the uncertainty in the forecast of the start of the rains is at some pre-agreed level. In short the science community needs to engage with the humanitarian system and its demand for information on risk. During this process, questions and solutions will arise as to what information is needed on the climate, what is producible and how it can be used. Possible starting positions for this dialogue include for climate science illustrating the variability and extent of uncertainty over time and space, associated with different climate forecasts and products. While for the humanitarian system this could include illustrating how an anticipatory risk management based humanitarian system will work in practice. For example exploring how the uncertainty in probabilistic climate information propagates through such decision support tools as predictive livelihood analysis (Levine et al 2011) and thus how it may lead to flexible and adaptive policies and actions. References: • Ajzen, I. 1991. The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes. 50: 179-211. • Interagency Resilience Working Group 2012. The Characteristics of Resilience Building: A discussion paper. Interagency Resilience Working Group. Interagency Resilience Working Group of the PPA Resilience Learning Partnership Group, Bond Disaster Risk Reduction Group & Bond Development and Environment Group. http://community.eldis.org/?233@@.5ad4406d!enclosure=.5ad4406e&ad=1 • Demeritt, D., Nobert, S., Cloke, H., and Pappenberger F., 2010. Challenges in communicating and using ensembles in operational flood forecasting. Meteorol. Appl. 17: 209–222. • FEWSNET (2010a) ‘Executive Brief: La Niña and Food Security in East Africa’, August 2010.

7 A number of vulnerability and capacity assessment tools exist that allow the capture of the degree to which

assistance may be needed (e.g. http://www.ifrc.org/en/what-we-do/disaster-management/preparing-for-

disaster/disaster-preparedness-tools/disaster-preparedness-tools/)

• FEWSNET (2010b) ‘East Africa Food Security Alert’, November 2, 2010. Pre-emptive livelihood support could mitigate likely La Niña impacts in the eastern Horn. • Hart, S.L. 1992. An Integrative Framework for Strategy-Making Processes. The Academy of Management Review, 17(2): 327-351 • Humanitarian Emergency Response Review 2011. Department For International Development (DFID). http://www.dfid.gov.uk/Documents/publications1/HERR.pdf • Hirschberg, P.A., and Abrams, E., 2011. American Meteorological Society Commission on the Weather and Climate Enterprise Board on Enterprise Communication A Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty Information. The American Meteorological Society. http://www.ametsoc.org/boardpges/cwce/docs/BEC/ACUF/2011-02-20-ACUF-Final-Report.pdf • Kniveton, D.R., Smith, C.D., and Black, R., 2012. Emerging migration flows in a changing climate in dryland Africa. Nature Climate Change, 10.1038/NCLIMATE1447. • Levine, S., Crosskey, A. and Abdinoor, M. 2011. System Failure: Revisiting the problems of • timely response to crises in the Horn of Africa. Humanitarian Practice Network, Network Paper No. 71, http://www.odihpn.org/hpn-resources/hpn-network-papers/system-failure-revisiting-problems-timely-response-crises-horn-of-africa • MacKenzie D. 1990. Inventing Accuracy: An Historical Sociology of Nuclear Missile Guidance. MIT Press: Cambridge, MA. • Moser, S. and Dilling, L. 2007. Toward the social tipping point: creating a climate for change. Creating a Climate for Change. Communicating Climate Change and Facilitating Social Change: 491-516 • Plummer, R. and Armitage, D. 2007. A resilience-based framework foeevaluating adaptive co-management: Linking ecology, economics and society in a complex world. Ecological Economics, 61: 62-74. • Price, J. and Leviston, Z. (in press). An integrated model of sustainable agriculture: understanding social and psychological influences on pro-environmental land management practice. Journal of Rural Studies. • Potgieter, A., and Bishop, J., 2001. Complex adaptive systems, emergence - The basics. University of Pretoria:http://people.cs.uct. ac.za/~yng/Emergence.pdf. • Rammel, C., Stagl, S., Wilfing, H., 2007. Managing complex adaptive systems — A co-evolutionary perspective on natural resource management. Ecological Economics, 63: 9 – 2 1. • Save the Children Fund and Oxfam 2012. A Dangerous Delay: The cost of late response to early warnings in the 2011 drought in the Horn of Africa. Joint Agency Briefing Paper 18. http://www.savethechildren.org.uk/sites/default/files/docs/Dangerous-delay-UK-version.pdf • Slingo, J. and Palmer, T. 2010. Uncertainty in weather and climate prediction. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369 (1956): 4751-4767. • Scoones, I. 2004. Climate Change and the Challenge of Non-equilibrium Thinking. IDS Bulletin, 35(3): 114–119. • Van den Bergh, J., Gowdy, J., 2003. The microfoundations of macroeconomics: An evolutionary perspective. Cambridge Journal of Economics 27, 65–84.

Figure 1. Shared decision space between climate scientists, the international humanitarian system and the communities affected by environmental hazards. In each sphere some of the factors that affect the different

spheres behaviour that may lie outside the immediate interaction of the epistemological communities.

Households and communities Climate scientists

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Reputation based around publications from mainly within the scientific community

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Figure 3. Hypothetical example of change in accuracy of seasonal forecast with time for different probabilities of below average rainfall.

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