%0 Journal Article %1 Brown2014 %A Brown, Simon J. %A Murphy, James M. %A Sexton, David M. H. %A Harris, Glen R. %D 2014 %J Climate Dynamics %K extreme myown non-stationary %N 9 %P 2681--2705 %R 10.1007/s00382-014-2080-1 %T Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases %U https://doi.org/10.1007/s00382-014-2080-1 %V 43 %X A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future ``observed'' extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO2. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9--42.1 °C for summer daily maximum temperature, 35.5--52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961--1990 of 35.7 °C, 42.1 and 78.4 mm respectively.

@article{Brown2014, abstract = {A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future ``observed'' extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO2. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9--42.1 {\textdegree}C for summer daily maximum temperature, 35.5--52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961--1990 of 35.7 {\textdegree}C, 42.1 and 78.4 mm respectively.}, added-at = {2024-02-02T12:06:02.000+0100}, author = {Brown, Simon J. and Murphy, James M. and Sexton, David M. H. and Harris, Glen R.}, biburl = {https://www.bibsonomy.org/bibtex/25b0f51a146aa8f81873028a6d9bb551e/simon.brown}, day = 01, description = {Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases | Climate Dynamics}, doi = {10.1007/s00382-014-2080-1}, interhash = {2f90dace33f1999caa18712790d273e7}, intrahash = {5b0f51a146aa8f81873028a6d9bb551e}, issn = {1432-0894}, journal = {Climate Dynamics}, keywords = {extreme myown non-stationary}, month = nov, number = 9, pages = {2681--2705}, timestamp = {2024-02-02T12:06:02.000+0100}, title = {Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases}, url = {https://doi.org/10.1007/s00382-014-2080-1}, volume = 43, year = 2014 }