Decadal Predictability of Surface Variables Over Europe and Relevance for the Energy Sector

Oral Presentation 

The timescale of decadal climate predictions, from a year-ahead up to a decade, is an important planning horizon for stakeholders in the energy-sector. With power systems transitioning towards a greater share of variable renewable energy sources, these systems become more vulnerable to the impacts of both climate variability and climate change. As decadal predictions sample both the internal variability of the climate and the externally forced response, these forecasts can provide useful information for the upcoming decade. 

There are two main ways in which decadal predictions can benefit the energy-sector. Firstly, they can be used to try to predict how a variable of interest, such as average temperature, may evolve over the coming year or decade. Secondly, a large ensemble of decadal predictions can be aggregated into a large synthetic event set to explore physically plausible extremes, such as winter wind droughts. 

For decadal predictions to prove useful, however, they must demonstrate skill during the hindcast (retrospective forecast) period. As a result, the initial phase of this PhD project has involved evaluating the skill of decadal predictions for surface variables, including temperature, wind speed, solar irradiance, and mean sea level pressure. We find high skill for decadal predictions (forecast years 2-9) for temperature, wind speed and solar irradiance over the UK and Northern Europe during the boreal winter (DJFM). These decadal predictions demonstrate skill at predicting the internal variability of the climate, capturing the variability around externally forced trends. From this, we demonstrate the potential for skillful decadal predictions for the energy-sector.