Resumen de Climate variability predictions for the wind energy industry: a climate services perspective
In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years.
The atmosphere interacts with other components of the Earth System such as the ocean, the cryosphere or the continental surface, that evolve more slowly than the atmosphere and drive the low-frequency variability. The natural climate oscillations that occur at those timescales impact wind speed and wind power generation. Therefore a better knowledge of how the wind resource varies at sub-seasonal, seasonal and decadal time scales is key to understand the risks that the electricity system is facing.
Anticipating this variability would also be helpful to many stakeholders in the energy sector to take precautionary actions. Forecasts at sub-seasonal, seasonal and decadal timescales are starting to be possible recently thanks to advances in climate modelling capabilities. Because climate variability is partly driven by coupled physical processes occurring in the Earth, numerical models that represent the interaction between different components of the Earth system can be employed to produce forecasts at these scales. The science of climate prediction deals with the challenge of producing predictions beyond meteorological timescales (i.e. weeks, months and years ahead) although not reaching the centennial timescales, which are studied with scenario-based climate projections.
Climate predictions employ the current state of the atmosphere, the ocean, the cryosphere, and the land surface to produce numerical integrations of each component and the forcings and interactions between them to model the evolution of the Earth system as a whole.
However, the usage of climate predictions in the wind power sector (or more generally in any specific decision-making context) poses a series of difficulties due to many complex aspects of this type of predictions. The efforts devoted in many initiatives to bring the needs of the users to the center of the discussion have given rise to the field of climate services. In order to assist decision-making, it is not only desirable to have the best predictions available but also to tailor them to the specific needs of each user. To achieve this goal, a dialogue with stakeholders needs to be established, and a transdisciplinary approach needs to be set up to take advantage of the developments in many research fields regarding knowledge transfer and communication.
The work presented in this dissertation advances the knowledge required to produce and successfully apply climate predictions to decision-making in the wind power sector and deals with the three aforementioned challenges: a) understanding the impact of climate oscillations at sub-seasonal and seasonal timescales on wind resource; b) developing methods to produce forecasts of wind speed and wind power generation at this scales; and c) facilitating the uptake of those predictions by means of a climate-services-based approach.
