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Supporting the climate adaptation strategy for Greater Manchester

Objectives: The University of Manchester used UKCP09 to obtain specific climate change information for Greater Manchester. The data was used to support the EcoCities project, which laid the foundations for the first blueprint of the city's integrated climate change adaptation strategy. The project focuses on the response of urban areas to the impacts of climate change and looks at how to adapt cities to the challenges and opportunities that a changing climate presents.

UKCP09 products used: Observed Data; The Weather Generator; The Threshold Detector.

How were UKCP09 products used?

1. Greater Manchester was divided into three climate zones, by analysing the UK Met Office 5 km gridded observed data. A check was made that the zones are homogenous in elevation and land use.

2. The Weather Generator was used to calculate seasonal averages for each climate zone. This was achieved by calculating an average for each 30-year period and then calculating the 10, 50 and 90th probability levels from the 100 averages. 

3. Climate extremes were calculated by identifying the 1st and 99th probability level from each of the 30-year data sets and then calculating the 10, 50 and 90th probability levels for each of these across the 100 files.

4. The Oxford Road Corridor was investigated at a finer resolution to analyse the impact of different development and greening scenarios on surface temperatures in the area. The climate projections were input into an energy balance model.

5. The Threshold Detector was also used to provide more information, including an investigation of Heating Degree Days, Cooling Degree Days, heat waves and heavy rainfall events likely to cause flooding.

Difficulties & limitations

One limitation of the UKCP09 projections is the 25 km standard gridded output. This grid does not respect the underlying climate, particularly in urban areas. For this reason, a climate zone analysis was completed, to provide a method that respected the spatial patterns in climate across the conurbation, driven largely by elevation.

The Threshold Detector is extremely useful for calculating known thresholds of certain weather events, such as heating degree days, however it is not possible to find out certain percentiles of interest, such as the 98th percentile summer maximum temperature without analysis of all of 100 generated files.

Lessons learned

The Weather Generator is a particularly useful tool for generating future daily climate data for a local area, both for visualising changes on a smaller scale, and for input into other models (e.g. the energy balance model used in this project). However, a significant amount of time is required to analyse the probabilistic outputs created.

The Threshold Detector is a useful time-saving tool for generating particular thresholds of interest. However, detailed local knowledge of the area is required in order for the user to specify certain thresholds, such as rainfall events likely to result in flooding. In this project, previous analysis was undertaken of rainfall records and flooding events to determine the threshold at which a flood event is likely to occur in Greater Manchester, as part of the LCLIP (available to download on the EcoCities website).

How were the results communicated?

The results were communicated to stakeholders at workshops and using presentations. The mapped outputs of Greater Manchester's climate projections were made available for stakeholders to view in the online blueprint, together with results from other EcoCities research. This included analysis of spatial vulnerability (social and infrastructure) across Greater Manchester.

The probabilistic projections are complex and therefore provide both an opportunity and a challenge to stakeholders to use and understand. This work aimed to provide clear, detailed and specific information for Greater Manchester. However, the level of probability that a stakeholder should use in planning and decision-making is still dependant on their individual perception of risk and cannot be decided by researchers.

Find out more

  • Contact details: Dr Gina Cavan, University of Manchester.