Impact of Spatio-Climatic Variability on Environment-Hosted Land-based Renewables: Microclimates

The following grants are linked: NE/H010343/1, NE/H010335/1, NE/H010351/1, NE/H01036X/1, NE/H010378/1 and NE/H010386/1

OBJECTIVES

We have no order of priority for the objectives: they are all equally important to the success of the consortium project.

1. To classify UK and western European weather states, derived from operational numerical weather prediction mode lsand climate simulations, in a form suitable for driving very high resolution climate down-scaling models.
2. To develop a predictive tool (micro- climate model) for obtaining very local climate predictions down to scales of less than one kilometre, using as its basis a state-of-the-art numerical weather prediction model applied in nested form at extreme resolutions.
3. To u se micro-climate predictions for specific wind farm sites to assess the effect of wind farms on peatland carbon balance and to compare the predictions with observations.
4. To further downscale the predictions from the micro-climate model using a very high resolution, turbine-resolving CFD model, to provide high resolution estimates of wind speed for the purposes of wind resource assess ment andto provide input to objectives 3 and 6.
5. To use the micro-climate predictions to quantify the effect of down-scaled meteorological data in complex terrain on crop development,growth, yield and harvestability.
6. To compare the impact on crop model predictions for bio-renewables in complex terrain of the newly-developed micro-climate model with existing empirically-d erived methods.
7. To validate the micro-climate and wind turbine resolving predictions using data from two dedicated field experiments which will quantify surface exchanges a t wind form and bio-renewables sites.
8. To disseminate the findings of this research programme to those with specialist and more general interest and in turn to benefit from their intellectual feedback.

Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemesand (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical,bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.

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