Northwest European Seasonal Weather Prediction from Complex Systems Modelling
Lead Research Organisation:
University of Lincoln
Department Name: School of Geography
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-Niño 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.
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-Niño 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.
Planned Impact
Ways in which potential beneficiaries may make use of the research
1. Meteorological services
We expect that meteorological/climatological services including the UK Met Office, European Centre for Medium-Range Weather Forecasts (ECMWF), US National Atmospheric & Oceanic Administration (NOAA), Icelandic Met Office (IMO), Danish Meteorological Institute (DMI) and Finnish Meteorological Institute (FMI), with whom we have a track record of successful collaboration (and continue to collaborate with most of the above on existing projects), will benefit from the improved understanding of Northwest European seasonal weather prediction to be gained through the research. Prof. Hanna and Dr. Hall collaborated on a University of Sheffield Project Sunshine research project with our Project Partner Prof. Adam Scaife (Head of Monthly to Decadal Prediction at the Met Office), who - with the potential of improving Met Office seasonal forecasts - writes in strong support of the proposal. We also have a direct link to the ECMWF through Co-I Dr. Weisheimer. Improving Northwest Europe seasonal weather forecasts will also help inform on the risk of occurrence of related extreme weather events (WP1.2). World Meteorological Organization reports show that mitigating mid-latitude extreme weather events - that are largely caused by jet-stream fluctuations which will be better understood through this project - is worth many billions of dollars.
2. End users of seasonal prediction
An important impact is the economic benefits of improved UK seasonal weather forecasts. Here we will focus on the agri-food industry, which we expect will benefit significantly from our improved seasonal forecasts. Cost-loss models assess the costs to decision-makers of taking action to protect against an adverse weather event, evaluated against losses that would be incurred if no action is taken (the cost-loss ratio). The probabilistic nature of the forecasts mean that users have different probability thresholds at which action becomes worthwhile in response to a forecast: we will work with agri-food partners (G Growers, Berry Gardens), Sainsbury's , the Environment Agency (EA), Centre for Ecology & Hydrology and the Met Office to (i) provide an analysis of these thresholds and (ii) assess economic value against the cost-loss ratio.
3. General public
Our study topic has a huge latent interest amongst the wider population. Seasonal forecasts are always of widespread interest, while the jet stream is a recently popular household buzzword, following much-publicised extreme weather events of the last decade. There is great scope to present our results to the general public to take opportunity of this interest as public impacts of highly variable seasons and extreme weather events continue to appear regularly in the media.
1. Meteorological services
We expect that meteorological/climatological services including the UK Met Office, European Centre for Medium-Range Weather Forecasts (ECMWF), US National Atmospheric & Oceanic Administration (NOAA), Icelandic Met Office (IMO), Danish Meteorological Institute (DMI) and Finnish Meteorological Institute (FMI), with whom we have a track record of successful collaboration (and continue to collaborate with most of the above on existing projects), will benefit from the improved understanding of Northwest European seasonal weather prediction to be gained through the research. Prof. Hanna and Dr. Hall collaborated on a University of Sheffield Project Sunshine research project with our Project Partner Prof. Adam Scaife (Head of Monthly to Decadal Prediction at the Met Office), who - with the potential of improving Met Office seasonal forecasts - writes in strong support of the proposal. We also have a direct link to the ECMWF through Co-I Dr. Weisheimer. Improving Northwest Europe seasonal weather forecasts will also help inform on the risk of occurrence of related extreme weather events (WP1.2). World Meteorological Organization reports show that mitigating mid-latitude extreme weather events - that are largely caused by jet-stream fluctuations which will be better understood through this project - is worth many billions of dollars.
2. End users of seasonal prediction
An important impact is the economic benefits of improved UK seasonal weather forecasts. Here we will focus on the agri-food industry, which we expect will benefit significantly from our improved seasonal forecasts. Cost-loss models assess the costs to decision-makers of taking action to protect against an adverse weather event, evaluated against losses that would be incurred if no action is taken (the cost-loss ratio). The probabilistic nature of the forecasts mean that users have different probability thresholds at which action becomes worthwhile in response to a forecast: we will work with agri-food partners (G Growers, Berry Gardens), Sainsbury's , the Environment Agency (EA), Centre for Ecology & Hydrology and the Met Office to (i) provide an analysis of these thresholds and (ii) assess economic value against the cost-loss ratio.
3. General public
Our study topic has a huge latent interest amongst the wider population. Seasonal forecasts are always of widespread interest, while the jet stream is a recently popular household buzzword, following much-publicised extreme weather events of the last decade. There is great scope to present our results to the general public to take opportunity of this interest as public impacts of highly variable seasons and extreme weather events continue to appear regularly in the media.
