Developing actionable seasonal climate information for the wind and solar energy email@example.com
This is a fully funded project and is not part of the DTP competition, applications should be made directly to the hosting department, see http://www.see.leeds.ac.uk/admissions-and-study/research-degrees/how-to-apply/ for more information.
This exciting PhD studentship offers a unique opportunity to work at the interface between the science of seasonal forecasting and applications within the renewable energy sector. The project offers the opportunity to work closely with the World Energy Meteorology Council (WEMC) and their partners, including a three month placement at WEMC.
The global capacity for energy generation by wind and solar technologies is currently around 650GW. This capacity is projected to double by 2020 making renewables a major component of the global energy landscape. Many renewable energy sources are particularly vulnerable to variations in weather and climate, such as generation by wind and solar technologies. There is therefore a need for accurate and reliable weather and climate information across a range of timescales from hours to years, so that energy companies can be better informed in their planning and decision-making. This includes their ability to account for the effects of weather and climate on energy supply/demand and on scheduling equipment maintenance. However, the current uptake of operational seasonal predictions by energy suppliers is generally low because of a perceived lack of skill and difficulty in interpreting forecast information, which both limit their usefulness.
While the ability to predict the weather up to a week ahead has improved steadily over the past few decades, predicting conditions for the forthcoming season has remained a major scientific challenge. However, there have been recent significant advances in predicting some of the major drivers of seasonal weather and climate variability in North America and Europe, such as the winter North Atlantic Oscillation (NAO) and the El Niño Southern Oscillation (ENSO). These advances in predictive skill have the potential to be translated into provision of more useful information for end-users in the renewable energy sector.
This PhD project will investigate how recent advances in seasonal prediction can be exploited to provide actionable information (e.g. on wind intensities) to end-users in the renewable energy sector. This will be achieved through strong engagement with CASE partner WEMC throughout the project. The focus will be on energy generation in North America and Europe, since these are regions where there have been advances in prediction capability and where there is substantial wind and solar energy capacity.
During the first year of the project, the student will undertake an extended placement with WEMC (based in Norwich, UK). The placement will be used to build relationships with several energy sector partners in North America and Europe and to determine their current use of seasonal prediction information and perceived barriers and enablers to their use of seasonal forecasts (see e.g. Figure 1). This information will be used to define the initial scope of the project, which will be adapted throughout the project via continued interaction with WEMC and its partners. The project therefore offers a unique opportunity to integrate social science techniques and industry engagement with core physical science.
Figure 1: Barriers and enablers to the use of seasonal climate forecasts (SCF) in Europe. Taken from Bruno Soares and Dessai (2016).
The PhD project will address two main questions that build on findings from the European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EUPORIAS; http://www.euporias.eu) project:
1) How do major drivers of seasonal climate variability (e.g. NAO, ENSO) affect predictive skill for wind and solar energy production, and how can these impacts be communicated to industrial partners?
2) What is the potential for combined (joint) predictive skill for wind and solar energy production and how are these affected by drivers such as the NAO and ENSO?
These questions will be investigated using predictions from several numerical models that have participated in the Climate-system Historical Forecast Project (CHFP). The student will evaluate the skill in different geographic regions of CHFP hindcasts for wind and surface insolation for summer (June-August) and winter (December-February) seasons using a variety of statistical techniques (see e.g. Figure 2). They will further quantify the effects of the NAO and ENSO on predictive skill. This analysis will be assisted by the Met Office project partner. The CHFP simulations will be compared to various observational based datasets to evaluate their performance (e.g. meteorological reanalysis datasets and, where available, station-based meteorological data). The student will subsequently consider the joint conditional skill for wind and solar energy production using select case studies.
Figure 2: An example of quantifying skill in predicting near-surface wind speeds across the UK as a function of forecast lead time. A skill score of 1 indicates a perfect forecast; 0 is equal to climatology, and therefore anything better than 0 is an improvement on climatology. Taken from Lynch et al. (2015).
The field of climate services is a new and expanding area. This project will explore the potential for seasonal forecast systems to provide actionable climate information to the renewable energy sector. The precise weight placed upon physical science aspects of prediction and predictability and/or climate services will depend on the particular interests of the student. Specific objectives could include:
- Analyse predictive skill for meteorological quantities relevant for wind and solar energies in state-of-the-art seasonal prediction systems and the dependence on season and region.
- Assess how major modes of climate variability (e.g. NAO, ENSO) affect predictive skill for energy applications.
- Assess conditional predictive skill for wind and solar energy applications.
- Explore the factors that currently limit the operational use of seasonal forecasting systems by end users in the renewable energy sector.
- Work with end users to develop prototype products for seasonal forecast applications to the renewable energy sector.
Potential for high impact outcome
Climate Services is a rapidly expanding discipline that explicitly addresses the increasing demand for weather and climate research to be tailored towards end-user applications. This studentship specifically addresses a key UK skills gaps identified by NERC, to provide interdisciplinary training to tackle current global challenges in Climate Services. The placement at WEMC and engagement with energy sector partners will produce unique opportunities to interact with industry, which is expected to lead to high impact outcomes both in terms of novel joint industry/academia publications and potential development of prototype forecasting products for industry.
The student will work under the supervision of Dr Amanda Maycock within the Physical Climate Change and Professor Suraje Dessai within the Climate Change Adaptation research groups in the School of Earth and Environment at the University of Leeds. These are world-leading physical and social science research groups, respectively, with an excellent track record in training PhD students and conducting high impact research. Both groups have strong connections to international policy-relevant programmes, such as CMIP6 and the IPCC. The student will spend a total of 3 months visiting WEMC, which is hosted at the University of East Anglia in Norwich. During the placement, the student will gain experience of WEMC’s operations and those of their partner organisations. They will have the opportunity to liaise with WEMC’s partners to identify the climate information needs of end-users in the energy sector.
The cross-disciplinary nature of the supervisory team and the research provides a unique opportunity to develop an interdisciplinary knowledge base encompassing physical science of prediction and predictability and social science techniques for engaging with end users.
This includes full specialist training in:
developing excellent scientific programming skills for processing and visualising large datasets (e.g. Python, Fortran, R);
developing statistical skills to assessing seasonal forecast skill and reliability;
learning social science techniques (e.g. expert elicitation) for interviewing private sector end users and identifying their operational needs for climate information;
developing excellent presentation and writing skills.
These skills will put the student in a strong position to pursue a successful career in academic research, but would also be highly desirable in the private sector. In addition, the student will also have access to a range of tailored training workshops that cover both technical and broader professional development skills. With this training, the student will be well equipped to pursue their own research interests.
The successful candidate will have ample opportunities to present their research at international scientific conferences (e.g. EGU, AGU), and to attend summer schools and workshops relevant to the research. Visits to the Met Office in Exeter are also planned.
The University of Leeds offers an excellent environment to undertake postgraduate research in Environmental Sciences. The Priestley International Centre for Climate supports interdisciplinary research across the University, including interactions between physical and social scientists. The Priestley centre hosts seminars and events that the student will be encouraged to participate in. The University of Leeds is part of the Met Office Academic Partnership, which provides a basis for collaboration between Met Office and Leeds researchers.
A good first degree (1st or high 2.1), Masters degree or equivalent in physical science, such as Physics, Mathematics, Meteorology, Environmental Science, Chemistry, or Computer Science. Read the general SPHERES DTP eligibility criteria here. Candidates for this project are not expected to have a specialist background in social sciences or climate services, but an interest in this area is essential and some broad awareness of these disciplines is also desirable.
Maycock AC; Keeley SPE; Charlton-Perez AJ; Doblas-Reyes FJ (2011) Stratospheric circulation in seasonal forecasting models: Implications for seasonal prediction, Climate Dynamics, 36, pp.309-321. doi: 10.1007/s00382-009-0665-x
Maycock AC; Hitchcock P (2015) Do split and displacement sudden stratospheric warmings have different annular mode signatures?, Geophysical Research Letters, 42, pp.10943-10951. doi: 10.1002/2015GL066754
Taylor AL; Dessai S; De Bruin WB (2015) Communicating uncertainty in seasonal and interannual climate forecasts in Europe, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373, doi: 10.1098/rsta.2014.0454
Bruno Soares M; Dessai S (2015) Exploring the use of seasonal climate forecasts in Europe through expert elicitation, Climate Risk Management, 10, pp.8-16. doi: 10.1016/j.crm.2015.07.001
Cannon, D., Brayshaw, D., Methven, J. and Drew, D. (2015) Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation. Meteorologische Zeitschrift. ISSN 0941-2948 (In Press)
Cannon, D.J., Brayshaw, D.J., Methven, J., Coker, P.J. and Lenaghan, D. (2015) Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain. Renewable Energy, 75. pp. 767-778. ISSN 0960-1481 doi: 10.1016/j.renene.2014.10.024
Lynch, K. J., Brayshaw, D. J. and Charlton-Perez, A. (2014) Verification of European subseasonal wind speed forecasts. Monthly Weather Review, 142 (8). pp. 2978-2990. ISSN 1520-0493 doi: 10.1175/MWR-D-13-00341.1
Bonjean Stanton MC; Dessai S; Paavola J (2016) A systematic review of the impacts of climate variability and change on electricity systems in Europe, Energy, 109, pp.1148-1159. doi: 10.1016/j.energy.2016.05.015
Bruno Soares M; Dessai S (2016) Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe, Climatic Change, 137, pp.89-103. doi: 10.1007/s10584-016-1671-8
Related undergraduate subjects:
- Computer science
- Environmental science
- Physical science