Details of Award
NERC Reference : NE/V001787/1
Northwest European Seasonal Weather Prediction from Complex Systems Modelling
Grant Award
- Principal Investigator:
- Professor E Hanna, University of Lincoln, School of Geography
- Co-Investigator:
- Professor S Pearson, University of Lincoln, Lincoln Inst for Agri-Food Technology
- Co-Investigator:
- Professor LC Shaffrey, University of Reading, Meteorology
- Co-Investigator:
- Dr H Wei, University of Sheffield, Automatic Control and Systems Eng
- Co-Investigator:
- Dr A Weisheimer, University of Oxford, Oxford Physics
- Co-Investigator:
- Professor E Hawkins, University of Reading, National Centre for Atmospheric Science
- Grant held at:
- University of Lincoln, School of Geography
- Science Area:
- Atmospheric
- Overall Classification:
- Panel B
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- Weather forecasting
- Tropospheric Processes
- Machine Learning (AI)
- Artificial Intelligence
- Abstract:
- The atmospheric circulation and jet stream (giant current of air) over the North Atlantic strongly influence seasonal weather conditions over Northwest Europe. Recent extreme seasons have been characterised by distinctive jet stream patterns, and jet strength and location is closely linked with extreme weather conditions experienced across the UK and Northwest Europe. Seasonal weather characteristics have major effects on people's livelihoods and the economy, for example about #1.5 billion in the UK in winter 2013/14, so producing reliable seasonal forecasts some months ahead would have significant benefits for society. Seasonal weather conditions also have major impacts on agriculture, food security, energy supply, public health/wellbeing, and severe weather planning. Until recently, North Atlantic atmospheric variability was thought to be largely due to unpredictable fluctuations. However, dynamical (that is, physics-based) seasonal forecasting systems run on giant supercomputers have led to some recent advances in forecasting skill, mainly for winter forecasts. Many factors appear to influence North Atlantic atmospheric circulation and jet-stream changes; possible influences can be broadly grouped into effects from variations in sea-ice extent and snow cover, North Atlantic sea-surface temperature variations, tropical influences such as the El-Ni?o Southern Oscillation, changes in the higher atmosphere (stratosphere) circulation, changes in energy from the Sun, and volcanic eruptions. These drivers of jet stream variability can oppose or reinforce one another, and there are indications of interactions between them. Drivers of jet-stream variability show seasonal variation, and distinctive drivers of jet-stream variability operate in different seasons. While some observed drivers can be reproduced in computer models of the climate system, improved understanding of more recently identified drivers of the North Atlantic jet stream is crucial for making progress in Northwest Europe seasonal climate predictions. The focus of government-funded research is on dynamical forecast systems; however, such forecasts are not always accurate. Furthermore, despite recent efforts to assess and improve their performance, dynamical model forecasts show little skill in summer. In the mid latitudes, including the UK and Northwest Europe, statistical forecasting has been neglected; however, recent developments in advanced statistical techniques, under the umbrella of 'machine learning', have taken place outside the climate-science community and are relatively quick and cheap to implement. There is thus considerable scope for applying complex statistical methods to the seasonal forecasting problem. Using a novel application of an established complex systems modelling approach called NARMAX (a type of machine learning, the results of which are highly interpretable), this project seeks to significantly improve current seasonal forecasts, extend skillful seasonal forecasting to seasons beyond winter, identify factors that contribute skill to the forecast, develop seasonal forecasts for Northwest Europe on a regional basis, and assess the benefits of skillful probabilistic seasonal forecasts to interested end users such as the agri-food industry. Our project plan effectively builds on promising pilot study results that we have recently published in the Quarterly Journal of the Royal Meteorological Society. Our novel application of NARMAX is likely to significantly improve forecast skill and help to inform development of the next generation of dynamical seasonal forecasting systems. We also seek to engage end users of seasonal forecasts, focusing mainly on the effects of improved seasonal forecasts on the agri-food industry: reflecting our links in this field but also because it has been relatively little studied compared with other key areas.
- NERC Reference:
- NE/V001787/1
- Grant Stage:
- Awaiting Completion
- Scheme:
- Standard Grant FEC
- Grant Status:
- Active
- Programme:
- Standard Grant
This grant award has a total value of £650,032
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|---|
| £13,014 | £236,253 | £71,018 | £73,247 | £228,972 | £24,052 | £3,475 |
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