Oxford Martin Programme on Resource Stewardship (OMPORS)
The Oxford Martin Programme on Resource Stewardship (OMPORS) is working across the sciences, social sciences and humanities to radically rethink global resource stewardship. The Programme aims to deliver a framework, accountable to future generations, that will create actionable input on critical global issues such as freshwater resources, land-use, biodiversity and atmosphere. These universally vital resources are subject to both cumulative and systemic pressures arising from human activities, which put them under threat of severe degradation and even depletion. Around the world these key resources are subject to a wide range of property rights and management regimes, the environmental efficacy and social equity of which are subject to competing ideological claims and disciplinary critiques. The Programme brings together an interdisciplinary team of philosophers, anthropologists, economists, modellers and environmental scientists to rethink how we monitor, manage, maintain and allocate globally important resources. The aim is to work through understandings of individual and collective behaviour and current institutional practice, with a focus on how technical information is used in decision-making, to deliver a new framework for stewardship that will ensure that the world’s essential resources remain available for generations to come.
The Usability of Probabilistic Forecasts
As part of the Oxford Martin Programme on Resource Stewardship, InSIS is collaborating with Atmospheric, Oceanic and Planetary Physics to conduct research on the Usability of Probabilistic Forecasts. The aim of this interdisciplinary project is to address why scientific information is – or is not - used in decision making for the management of natural hazards and resources. It will examine current practices, techniques and methodologies employed throughout the weather forecast and user network,  identifying factors that promote, enable or constrain the successful use of probabilistic forecasts, and the extent to which forecast information quality and/or institutional practices contribute to these forecasts [not] being used.
In recent years the availability of ensemble-based  probabilistic weather forecasts has increased, but the factors that influence their usability in a range of organisations involved in their production, processing and interpretation is not well understood. What can we learn from mismatches, failures and successes in the use of probabilistic forecasts? How can information be used effectively, despite varying levels of uncertainty and risk in the reliability, accuracy and reputation of science, modelling and forecasting efforts?
Relevant factors may include:
● the characteristics (e.g. lead times, spatial resolution) and quality (e.g. reliability, sharpness) of the information provided;
● institutional factors (e.g. legal frameworks, political priorities, approaches to risk and accountability, existing infrastructures and available resources); and
● issues relating to communication, interpretation and decision-making in probabilistic frameworks.
Natural and social scientists will work together to explore how such factors influence the forecast and user network in real-world contexts, through case studies in a range of institutional and geographical settings. This comparative case study structure aims to facilitate understanding of commonalities and differences among and between forecast producers, processors and interpreters. These case studies will incorporate multidisciplinary approaches:
● A quantitative science-led approach will consider whether the forecasts produced [operationally] meet the performance requirements of the forecast-user for different applications
● An ethnographic approach will provide sustained insights into the real-world situations in which forecasts are produced and interpreted, and resource management decisions made
● A cross-disciplinary approach will consider the integrated challenges from these two areas, exploring additionally their relationships and interactions and the potential for improving the utilisation of probabilistic forecasts.
Potential Case Studies
To meet the goals of this a number of project case studies are currently being prepared that will facilitate structured comparisons, for example, to identify commonalities and differences between the response of a range of [private, NGO or public sector] organisations to early warnings of drought and flood.
We expect the exact nature of these case studies to be determined by the end of 2013, and we would be pleased to discuss any potential collaborations in the upcoming months.
Prof Steve Rayner and Dr Sophie Haines (Institute for Science, Innovation and Society)
Prof Tim Palmer and Dr Liz Stephens (Atmospheric, Oceanic and Planetary Physics)
We define this as encompassing the producers, processers, and interpreters that contribute to the wider exchange of information from forecast model to decision-maker.
 Ensemble: a group of models or group of simulations generated with different models or model inputs.