%0 Journal Article %1 Murphy2007Methodology %A Murphy, J. M. %A Booth, B. B. B. %A Collins, M. %A Harris, G. R. %A Sexton, D. M. H. %A Webb, M. J. %D 2007 %I The Royal Society %J Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences %K ukcp climatechange colleagues model ensembles qump %N 1857 %P 1993--2028 %R 10.1098/rsta.2007.2077 %T A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles %U http://dx.doi.org/10.1098/rsta.2007.2077 %V 365 %X A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean–atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales.

@article{Murphy2007Methodology, abstract = {A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean–atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Murphy, J. M. and Booth, B. B. B. and Collins, M. and Harris, G. R. and Sexton, D. M. H. and Webb, M. J.}, biburl = {https://www.bibsonomy.org/bibtex/2738743804df0f532b9ae876b9a56bbc2/pbett}, citeulike-article-id = {2281072}, citeulike-linkout-0 = {http://dx.doi.org/10.1098/rsta.2007.2077}, citeulike-linkout-1 = {http://rsta.royalsocietypublishing.org/content/365/1857/1993.abstract}, citeulike-linkout-2 = {http://rsta.royalsocietypublishing.org/content/365/1857/1993.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/17569653}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=17569653}, comment = {Describes the methodology used in "UKCPI08" (which became UKCP09).}, day = 15, doi = {10.1098/rsta.2007.2077}, interhash = {8a1bc471b665ba2e96bf53b0e69844ae}, intrahash = {738743804df0f532b9ae876b9a56bbc2}, issn = {1471-2962}, journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}, keywords = {ukcp climatechange colleagues model ensembles qump}, month = aug, number = 1857, pages = {1993--2028}, pmid = {17569653}, posted-at = {2011-11-17 16:07:06}, priority = {2}, publisher = {The Royal Society}, timestamp = {2018-06-22T18:32:06.000+0200}, title = {A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles}, url = {http://dx.doi.org/10.1098/rsta.2007.2077}, volume = 365, year = 2007 }