This three-year consortium project - involving the universities of Leeds, Exeter, Edinburgh, Newcastle, Liverpool, Reading, as well as the London School of Economics and the Plymouth Marine Laboratory - will begin in the next few months. It brings together the UK climate modelling, statistical modelling, and impacts communities to work closely together for the first time on developing risk-based prediction for decision making in the face of climate variability and change. Project partners include the Met Office, Environment Agency, an NGO and UoL Africa College. Additional partners are welcome to become members of the EQUIP network. Our work will feed directly into future IPCC and Met Office assessments of climate change.
The project will advance the quantification of uncertainty in the prediction of climate and of climate impacts with a view to supporting decision making among users. It will develop new methodologies for assessing the information content of climate-model projections, for combining climate models and data-driven models to support decisions, and for evaluating the quality of climate and impacts predictions. The project will also conduct integrated assessments of the cascade of uncertainty from climate to impacts: not just feeding climate ensembles through impact models, but analysing sources of uncertainty and using the resulting information to find better ways of quantifying uncertainty in predictions of climate impacts for decision makers. The lead PI for the project is Andy Challinor, with Chris Ferro also playing a leading role. Other PIs are Lenny Smith, Andy Morse, Myles Allen, Gabi Hegerl and Icarus Allen.
The project will:
* Develop advances in methodology for risk-based prediction, quantification of uncertainty and identification of information content that will be used widely by the scientific community, including UKCIP.
* Inform policy through improved predictions of near-term climate change and its impacts, including information on the relevance and applicability of the predictions.
* Engage users in order to ensure that maximum utility is gained from climate science.
* Contribute significantly to the fifth assessment report of the IPCC, through improved quantification of uncertainty across climate and its impacts and through sets of impacts projections based on AR5 model simulations conducted elsewhere.
* Make a significant contribution to directed NERC research on quantifying uncertainty in predictions of regional and local climate change and climate impacts, by addressing the Natural Hazards and Climate System Theme Action Plans.
The specific objectives are: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.
WP1: Targeted and informative forecast system design
Lead: Lenny Smith (LSE)
Contributing: Mat Collins (MOHC)
Role: to work directly with users and with other WPs to develop new approaches to the design of ensemble prediction systems that focus on information content and utility.
WP2: Methods for evaluating climate forecasting systems
Lead: Chris Ferro (Exeter)
Contributing: David Stephenson (Exeter), Simon Tett (Edinburgh)
Role: to develop new methods for evaluating climate and impacts predictions, and to support the use of these methods to evaluate the predictions produced by other WPs.
WP3: User engagement
Lead: Andy Morse (Liverpool)
Contributing: Suraje Dessai (Exeter), Chris Kilsby (Newcastle), Robert Willows (EA), Mike Edwards (CAFOD)
Role: to ensure that the project interacts effectively with a wide variety of potential beneficiaries in both the public and private sectors.
WP4: Implementation of climate prediction systems
Lead: Myles Allen (Oxford)
Contributing: Mat Collins (MOHC)
Role: to work with other WPs in implementing uncertainty analyses and prediction systems and to conduct novel uncertainty analysis climate variables for AR5.
WP5: Quantifying future risk to crop production
Lead: Andy Challinor (Leeds)
Contributing: PI Ed Hawkins and Researcher Co-I Tom Osborne (Reading)
Role: to work with WP1-4 to address project objectives 2-7 for the case of crop production.
WP6: Impacts of changes in droughts and heat waves
Lead: Gabi Hegerl (Edinburgh)
Contributing: Chris Kilsby (Newcastle), Peter Stott (MOHC), Simon Tett (Edinburgh), Ed Hawkins (Reading), Robert Willows (EA)
Role: to work with WP1-4 to address project objectives2-7 for the case of droughts and heat waves.
WP7: Quantifying Uncertainty in Marine Ecosystems
Lead: Icarus Allen (Plymouth)
Role: to work with WP1-4 to address project objectives 2-7 for the case of marine ecosystems.