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Specific Objectives

Specific Objectives

Specific Objectives

The specific objectives were:

  1. Develop new ways to increase and assess the utility of climate prediction for decision makers. Climate change prediction is an extrapolatory problem and consequently we have no observations with which to verify or confirm a forecasting system. Nevertheless there are a number of avenues to pursue to assess the strengths and weaknesses of different approaches and to build necessary, if not sufficient, conditions for utility, through which we might gain greater confidence in statements about future climate and its impacts. This work will build on experience and expertise in the field of non-linear dynamics, and will study questions of how observations of the past might be best used to provide one of these necessary but not sufficient conditions.
  2. Improve current approaches to quantifying uncertainty in predictions of climate impacts. This will be achieved through application to the climate-and-impacts system of the methods commonly used to quantify uncertainty in climate prediction (e.g. ensemble weighting, Bayesian analysis). This relatively simple transfer of methods will improve impacts projections through improved use of both climate ensembles and observations of impacts variables. Specific issues addressed include the development of appropriate weights for ensemble impacts prediction: does weighting on impacts skill improve the quality of projections over weights based on climate? This implies effective combination of observations of climate and its impacts with models. Further improvements in methodology are likely to be made through the increased understanding of the cascade of uncertainty associated with objective 5.
  3. Construct comparable sets of risk-based climate and impacts predictions. These predictions will be based upon the new approaches and systems developed in the project. They will include an assessment of the relationship between predictability and spatial scale (i.e. reliability of regional vs continental scale predictions).
  4. Develop a framework for evaluating the performance of climate and impacts predictions. There are two reasons for evaluating climate and impacts predictions in the project: to inform assessments of the trustworthiness and value of future predictions by quantifying the performance of climate prediction systems when applied to historical prediction problems; and to inform the use of predictions for making decisions and for improving prediction systems by furthering our understanding of sources of uncertainty. Unlike weather forecasts, the performance of historical climate predictions may be an unreliable guide to future performance because the climate system is evolving into previously unrecorded states. Additional obstacles include the small numbers of climate predictions and commensurate observations that are available for evaluation, and how to account for the effects on performance of past evolution of the climate system. In meeting this objective, therefore, we shall develop the new tools that are required for evaluating the climate and impacts predictions produced in the project.
  5. Further understanding of the cascade of uncertainty from climate to impacts and its relationship to model error and climate predictability. Uncertainty in climate simulation, model error and the predictability of climate have implications for the predictability of climate impacts. Furthermore, non-linearities in the response of impacts variables (e.g. crop failure resulting from a few days of elevated temperature during flowering) necessitate understanding of a broad range of non-climatic uncertainties (e.g. when the crop flowers, the likelihood of high temperatures during this period and the impact of those temperatures). By increasing our understanding of this cascade of uncertainty, the situations in which climate models can produce useful information, and those in which they cannot, will be identified. Thus the situations in which uncertainty prevents skilful forecasts of climate impacts will be identified.
  6. Interact with users to inform developments and to guide the use of climate and impacts predictions. A range of users, from the insurance sector to the development NGOs, have expressed a strong interest in this project (see letters of support and Impact Plan). Through early engagement (at the small conference in month 6), and continual involvement with the project, these users will be better equipped to understand and use climate information, and EQUIP scientists will have a greater understanding of the needs of these users.
  7. Grow the community of scientists and users who collaborate to quantify uncertainty in climate and impacts predictions. This project brings together the UK climate modelling, statistical modelling, and impacts communities to work closely together for the first time on quantifying uncertainty and developing risk-based prediction for decision making. Internal and external collaboration are an integral part of the project, with our activities disseminated through a web site and a conference at the end of the project.