Tim Summers, Erik Mackie, Risa Ueno, Charles Henry Simpson, J. Scott Hosking , Tudor Suciu, Andrew Coburn, Emily Shuckburgh 

Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments however are measures of localised impacts, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as “disruption days”, as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5-20 year time horizon which can be recognised in business planning.

We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5, and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias-corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst case scenario planning.