James Norman

James Norman


I am a PhD candidate based the School of Earth and Environment at the University of Leeds. My research seeks to understand how the latest seasonal climate prediction tools can be used to inform decision making within the energy industry. I am supervised by Dr Amanda Maycock and co-supervised by Professor Suraje Dessai. I also collaborate with a Professor Alberto Troccoli, director of the World Energy Meteorology Council (WEMC).

Prior to enrolling as a PhD student, I worked as a Research Translation Fellow on the Yorkshire Integrated Catchment Solution’s Project (iCASP) and as a Research Assistant supporting the Climate Change Adaptation Group within the Sustainability Research Institute (SRI) at the University of Leeds. Before relocating to Yorkshire, I completed an interdisciplinary Master’s degree at the University of Copenhagen and focussed my thesis project work on the quantification of uncertainty in centennial climate projections. Prior to undertaking my postgraduate studies, I worked as an environmental consultant, as a research assistant at a think-tank and within product development at a clean-tech start-up.

Royal Meteorological Society - Student Associate Fellow

  • Decision-making under uncertainty
  • Climate prediction across timescales
  • Novel commercial applications of earth imaging and climate information products
  • Low carbon transition and renewables
  • Developing actionable seasonal climate information for the wind and solar industry

Research interests

My PhD project will investigate how seasonal climate predictions can be used to provide actionable information to different players in European and North American energy systems.

Renewables now account for the majority (two-thirds in 2016) of newly installed electrical power generation globally. Sustaining this growth requires the successful integration of renewables into electricity networks and power markets; primarily a task of managing the variable output such that demand is met in real time. Weather and climate prediction systems will therefore become increasingly valuable tools within the energy system, informing planning, investment and operational decisions on timescales of hours to years. Significant predictive skill of energy-relevant variables on seasonal timescales in the mid-latitudes has been demonstrated with contemporary forecast systems. However, uptake of seasonal predictions within the energy sector is generally low because of a perceived lack of forecast skill and difficulty in interpreting forecast information. Capturing the value of enhanced foresight capabilities therefore requires further translation of forecast information for the specific contexts and needs of end-users.

With this background, the following research questions are the subject of my current investigations:

  1. How do major drivers of seasonal climate variability (e.g. NAO, ENSO) affect predictive skill for wind and solar energy production?
  2. What is level of co-variability in wind, solar and the demand profile, and what is the potential for combined (joint) predictive skill?
  3. What are the decision contexts and information needs found amongst energy system stakeholders? What are the actual and perceived barriers and enablers to their use of seasonal forecasts?
  4. And how can forecast products be tailored and communicated to better meet user needs?


  • MSc, Climate Change, University of Copenhagen
  • BSc (Hons), Physics, University of Bristol

Research groups and institutes

  • Institute for Climate and Atmospheric Science
  • Sustainability Research Institute