Serial clustering of extratropical cyclones in a multi-model ensemble of historical and future simulations

• Published in: Quarterly Journal of the Royal Meteorological Society
• Main Authors: Economou, Theodoros, Stephenson, David B., Pinto, JG, Shaffrey, LC, Zappa, G
• Other Authors: Gray, L
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley / Royal Meteorological Society 2015
• Subjects: Stephenson; North Atlantic; North Atlantic oscillation
• Online Access: http://hdl.handle.net/10871/18876; https://doi.org/10.1002/qj.2591

Accepted Article This is the peer reviewed version of the following article: Economou, T., Stephenson, D. B., Pinto, J. G., Shaffrey, L. C. and Zappa, G. (2015), Serial clustering of extratropical cyclones in a multi-model ensemble of historical and future simulations. Q.J.R. Meteorol. Soc. , which has been published in final form at http://dx.doi.org/10.1002/qj.2591. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion ...