Bauer, P., Geer, A. J., Lopez, P., and Salmond, D. (2010). Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Q. J. R. Meteorol. Soc., 136, 1868–1885, doi: 10.1002/qj.659.CrossRefGoogle Scholar

Berner, J., Shutts, G. J., Leutbecher, M., and Palmer, T. N. (2009). A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626, doi: 10.1175/2008JAS2677.1.CrossRefGoogle Scholar

Bonavita, M., Raynaud, L., and Isaksen, L. (2011). Estimating background-error variances with the ECMWF Ensemble of Data Assimilations system: Some effects of ensemble size and day-to-day variability. Q. J. R. Meteorol. Soc., 137, 423–434, doi: 10.1002/qj.756.CrossRefGoogle Scholar

Buizza, R., Miller, M., and Palmer, T. N. (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc., 125, 2887–2908, doi: 10.1002/qj.49712556006.CrossRefGoogle Scholar

Buizza, R. and Palmer, T. N. (1995). The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 1434–1456, doi: 10.1175/1520–0469(1995)052<1434:TSVSOT>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar

Courtier, P., Thépaut, J.-N., and Hollingsworth, A. (1994). A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc., 120, 1367–1387, doi: 10.1002/qj.49712051912.Google Scholar

Dee, D. P., and Co-authors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597, doi: 10.1002/qj.828.CrossRefGoogle Scholar

Dee, D. P. and Uppala, S. (2009). Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Q. J. R. Meteorol. Soc., 135, 1830–1841, doi: 10.1002/qj.493.CrossRefGoogle Scholar

Derber, J. C. and Wu, W.-S. (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 2287–2299, doi: 10.1175/1520–0493(1998)126<2287:TUOTCC>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar

Fisher, M., Trémolet, Y., Auvinen, H., Tan, D., and Poli, P., 2011: Weak-constraint and long window 4D-Var. ECMWF Tech. Memo., 655, ECMWF, Reading, United Kingdom, 47 pp.Google Scholar

Forbes, R. M., Tompkins, A. M., and Untch, A. (2011). A new prognostic bulk microphysics scheme for the IFS. ECMWF Tech, Memo., 649, ECMWF, Reading, United Kingdom, 30 pp.Google Scholar

Geer, A. J., Bauer, P., and Lopez, P. (2010). Direct 4D-Var assimilation of all-sky radiances. Part II: Assessment. Q. J. R. Meteorol. Soc., 136, 1886–1905, doi: 10.1002/qj.681.CrossRefGoogle Scholar

Isaksen, L., Bonavita, M., Buizza, R., et al. (2010). Ensemble of Data Assimilations at ECMWF. ECMWF Tech. Memo., 636, ECMWF, Reading, United Kingdom, 48 pp.Google Scholar

Hoskins, B. J., McIntyre, M. E., and Robertson, A. W. (1985). On the use and significance of isentropic potential vorticity maps. Q. J. R. Meteorol. Soc., 111, 877–946, doi: 10.1002/qj.49711147002.CrossRefGoogle Scholar

Janisková, M., and Lopez, P. (2012). Linearized physics for data assimilation at ECMWF. ECMWF Tech. Memo., 666, ECMWF, Reading, United Kingdom, 26 pp.Google Scholar

Joos, H. and Wernli, H. (2012). Influence of microphysical processes on the potential vorticity development in a warm conveyor belt: a case-study with the limited-area model COSMO. Q. J. R. Meteorol. Soc., 138, 407–418, doi: 10.1002/qj.934.CrossRefGoogle Scholar

Jung, T., Balsamo, G., Bechtold, P., et al. (2010). The ECMWF model climate: Recent progress through improved physical parametrizations. Q. J. R. Meteorol. Soc., 136, 1145–1160, doi: 10.1002/qj.634.CrossRefGoogle Scholar

Jung, T., and Co-authors (2012). High-resolution global climate simulations with the ECMWF model in project Athena: Experimental design, model climate, and seasonal forecast skill. J. Climate, 25, 3155–3172, doi:10.1175/JCLI-D-11-00265.1.CrossRefGoogle Scholar

Klinker, E., and Sardeshmukh, P. D. (1992). The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608–627, doi:10.1175/1520–0469(1992)049<0608:TDOMDI>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar

Klocke, D. and Rodwell, M. J. (2014). A comparison of two numerical weather prediction methods for diagnosing fast-physics errors in climate models. Q. J. R. Meteorol. Soc., 140, 517–524, doi: 10.1002/qj.2172.CrossRefGoogle Scholar

Leroy, S. S., and Rodwell, M. J. (2014). Leveraging highly accurate data in diagnosing errors in atmospheric models, Bull. Am. Meteorol. Soc., doi: 10.1175/BAMS-D-12-00143.1.CrossRefGoogle Scholar

Leutbecher, M., and Palmer, T. (2008). Ensemble forecasting. J. Comp. Phys., 227, 3515–3539, doi: 10.1016/j.jcp.2007.02.014.CrossRefGoogle Scholar

Lorenz, E. N. (1963). Deterministic non-periodic flow. J. Atmos. Sci., 20, 130–141, doi:10.1175/1520–0469(1963)020<0130:DNF>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar

Magnusson, L., Bidlot, J.-R., Lang, S., Thorpe, A., and Wedi, N. (2014). Evaluation of medium-range forecasts for hurricane Sandy. Mon. Wea. Rev., 142, 1962–1981, doi:10.1175/MWR-D-13-00228.1.CrossRefGoogle Scholar

Mapes, B. E. and Bacmeister, J. T. (2012). Diagnosis of tropical biases and the MJO from patterns in the MERRA analysis tendency fields. J. Climate, 25, 6202–6214, doi: 10.1175/JCLI-D-11-00424.1.CrossRefGoogle Scholar

Madonna, E. (2013). Warm conveyor belts: Climatology and forecast performance. PhD thesis. Diss, 21315, ETH, Zurich, Switzerland, 143 pp.Google Scholar

Rodwell, M. J. and Jung, T. (2008). Understanding the local and global impacts of model physics changes: an aerosol example. Q. J. R. Meteorol. Soc., 134, 1479–1497. doi: 10.1002/qj.298.CrossRefGoogle Scholar

Rodwell, M. J., Magnusson, L., Bauer, P., et al. (2013). Characteristics of occasional poor medium-range weather forecasts for Europe. Bull. Am. Meteorol. Soc., 94, 1393–1405, doi: 10.1175/BAMS-D-12-00099.1.CrossRefGoogle Scholar

Rodwell, M. J. and Palmer, T. N. (2007). Using numerical weather prediction to assess climate models. Q. J. R. Meteorol. Soc., 133, 129–146, doi: 10.1002/qj.23.CrossRefGoogle Scholar

Shutts, G., Leutbecher, M., Weisheimer, A., et al. (2011). Representing model uncertainty: Stochastic parametrization at ECMWF. ECMWF Newsletter, 129, ECMWF, Reading, United Kingdom, 19–24.Google Scholar

Simmons, A. J., Burridge, D. M., Jarraud, M., Girard, C., and Wergen, W. (1989). The ECMWF medium-range prediction models development of the numerical formulations and the impact of increased resolution. Meteor. Atmos. Phys., 40, 28–60, doi: 10.1007/BF01027467.CrossRefGoogle Scholar

Thorpe, A. J. (1986). Synoptic scale disturbances with circular symmetry. Mon. Wea. Rev., 114, 1384–1389, doi: 10.1175/1520–0493(1986)114<1384:SSDWCS>2.0.CO;2.2.0.CO;2>CrossRefGoogle Scholar

Trémolet, Y. (2007). Model-error estimation in 4DVar. Q. J. R. Meteorol. Soc., 133, 1267–1280, doi:10.1002/qj.94.CrossRefGoogle Scholar

Chen, B., Qian, Z., and Zhang, L. (1996). Numerical simulation of the formation and development of vortices over Qinghai-Xizang Plateau in summer. Scientia Atmospherica Sinica, 20, 491–502. (in Chinese)Google Scholar

Chen, Z., Min, W., and Xu, M. (2004). Mesoscale characteristics of the unbalanced force of atmospheric motion and environmental fields of rain storm on 20–21 July 1998. Acta. Meteor. Sinica., 62, 375–383. (in Chinese)Google Scholar

Cui, X., Wu, G., and Gao, S. (2002). Numerical simulation and isentropic analysis of frontal cyclone over the western Atlantic Ocean. Acta. Meteor. Sinica., 60, 385–399. (in Chinese)Google Scholar

Cui, X., Goa, S., and Wu, G., (2003). Up-sliding slantwise vorticity development and the complete vorticity equation with mass forcing. Adv. Atmos. Sci., 20(5), 825–836.Google Scholar

Dee, D.P., Uppala, S.M., Simmons, A.J., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteo. Soc., 137, 533–597.CrossRefGoogle Scholar

Ding, Y. (1993). Monsoons over China. Beijing: Springer.Google Scholar

Ding, Y. (2005). Advanced Synoptic Meteorology. Beijing: China Meteorological Press. (in Chinese)Google Scholar

Ding, Z. and Lu, J. (1990). A numerical experiment on the eastward movement of a Qinghai-Xizang Plateau vortex. J. Nanjing Institute of Meteorology, 13, 426–433. (in Chinese)Google Scholar

Ertel, H. (1942). Ein neuer hydrodynamischer Wirbelsatz. Meteorology Z. 59, 33–49.Google Scholar

Gao, S., Wang, X., and Zhou, Y. (2004). Generation of generalized moist potential vorticity in a frictionless and moist adiabatic flow. Geophys. Res. Lett., 31, L12113.CrossRefGoogle Scholar

Group of Tibetan Plateau Low System (1978). The preliminary research on the formation and development of the Tibetan Plateau vortex in boreal summer. Sci. China (Ser. A), 3, 341–350. (in Chinese)Google Scholar

Hoskins, B., Mclntyre, M., and Robertson, A. (1985). On the use and significance of isentropic potential vorticity maps. Quart. J. Roy. Meteor. Soc., 111, 877–946.CrossRefGoogle Scholar

Huffman, G., Adler, R., Bolvin, D., et al. (2007). The TRMM multi-satellite precipitation analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeor., 8, 38–55.CrossRefGoogle Scholar

Jiang, Y., Chen, Z., and Zhou, Z. (2004). Slantwise vorticity development and meso-β scale low vortex. Journal of PLA University of Science and Technology, 5, 81–87. (in Chinese)Google Scholar

Li, G. (2002). The Tibetan Plateau Dynamic Meteorology. Beijing: China Meteorology Press. (in Chinese)Google Scholar

Liu, F. and Fu, M. (1985). A study on the eastward moving lows over Qinghai-Xizang Plateau. Plateau Meteorology. 5, 125–134. (in Chinese)Google Scholar

Liu, Y.M., Wu, G.X., Liu, H., and Liu, P. (2001). Condensation heating of the Asian summer monsoon and the subtropical anticyclone in Eastern Hemisphere. Clim. Dyn., 17(4), 327–338.CrossRefGoogle Scholar

Ma, L., Qin, Z., and Duan, Y. (2002). Case study on the impact of atmospheric baroclinicity to the initial development of Jianghuai cyclones. Acta. Oceanologica. Sinica., 24, 95–104.Google Scholar

McGregor, J. (1993). Economical determination of the departure points from the Semi-Lagrangian models. Mon. Wea. Rev., 121, 221–230.2.0.CO;2>CrossRefGoogle Scholar

Qiao, Q. (1987). The environment analysis on 500 hPa vortexes moving eastward out of Tibetan Plateau in summer. Plateau Meteorology. 6, 45–55. (in Chinese)Google Scholar

Qiao, Q. and Zhang, Y. (1994). Synoptic Meteorology of the Tibetan Plateau. Beijing: China Meteorology Press. (in Chinese)Google Scholar

Shen, R., Reiter, E., and Bresch, J. (1986). Numerical simulation of the development of vortices over the Qinghai-Xizang (Tibet) Plateau. Meteor. Atmos. Phys., 35, 70–95.CrossRefGoogle Scholar

Sun, G. and Chen, B. (1988). Dynamic processes of the moving and development low over Qinghai-Xizang Plateau during the early summer. Journal of Chinese Academy of Meteorological Sciences. 3, 56–63. (in Chinese)Google Scholar

Tao, S. (1980). Chinese Rainstorms. Beijing: Science Press. (in Chinese)Google Scholar

Tao, S. and Ding, Y. (1981). Observational evidence of the influence of the Qinghai-Xizang (Tibet) Plateau on the occurrence of heavy rain and severe convective storms in China. Bull. Amer. Meteor. Soc., 62, 23–30.2.0.CO;2>CrossRefGoogle Scholar

Wang, Y., Wang, Y., and Zhang, L. (2007). The development of slantwise vorticity near a weakened tropical cyclone. Journal of Tropical Meteorology, 23, 47–52. (in Chinese)Google Scholar

Wu, G., Cai, Y., and Tang, X. (1995). Moist potential vorticity and slantwise vorticity development. Acta Meteor. Sinica, 53, 387–405. (in Chinese)Google Scholar

Wu, G. and Liu, H. (1997). Vertical vorticity development owing to down-sliding at slantwise isentropic surface. Dyn. Atmos. Ocean., 27, 715–743.CrossRefGoogle Scholar

Wu, G. and Liu, Y. (2000). Thermal adaptation, overshooting, dispersion, and subtropical anticyclone. Part I: Thermal adaptation and overshooting. Chinese J. Atmos. Sci., 24, 433–446. (in Chinese)Google Scholar

Wu, G., Zheng, Y., and Liu, Y. (2013). Dynamical and thermal problems in vortex development and movement. Part II: Generalized slantwise vorticity development. Acta Meteor. Sinica, 27(1), 15–25, doi: 10.1007/s13351-013-0102–2CrossRefGoogle Scholar

Ye, D. and Gao, Y. (1979). The Tibetan Plateau Meteorology. Beijing: Science Press. (in Chinese)Google Scholar

Zhang, J., Zhu, B., and Zhu, F. (1988). Progresses in Tibetan Plateau Meteorology. Beijing: Science Press. (in Chinese)Google Scholar

Zheng, Y., Wu, G., and Liu, Y. (2013). Dynamical and thermal problems in vortex development and movement. Part I: A PV-Q View. Acta Meteor. Sinica, 27(1), 1–14, doi: 10.1007/s13351-013-0101–3.CrossRefGoogle Scholar

Allan, R. P. et al. (2014). Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv. Geophys., 35, 533–552.CrossRefGoogle Scholar

Allen, M. R. (2003). Liability for climate change. Nature, 421, 891–892.CrossRefGoogle ScholarPubMed

Allen, M. R. and Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, part I: theory. Clim. Dyn., 21, 477–491.CrossRefGoogle Scholar

Baede, A. P. M. (2001). The climate system: an overview. In Climate Change 2001, The Scientific Basis, eds. Houghton, J. T. et al. Cambridge University Press, pp. 85–98.Google Scholar

Bindoff, N. L. et al. (2013). Detection and attribution of climate change: from global to regional. In Climate Change 2013: The Physical Science Basis, eds. Stocker, T. F. et al. Cambridge University Press, pp. 867–952.Google Scholar

Blackburn, M. and Hoskins, B. J. (2001). Atmospheric variability and extreme autumn rainfall in the UK. Available at <http://www.met.reading.ac.uk/mike/autumn2000.html>..>Google Scholar

Chase, T. N., Wolter, K., Pielke, R. A. Sr., and Rasool, I. (2006). Was the 2003 European summer heat wave unusual in a global context? Geophys. Res. Lett., 33, L23709.CrossRefGoogle Scholar

Christidis, N., Stott, P. A., Zwiers, F. W., Shiogama, H., and Nozawa, T. (2010). Probabilistic estimates of recent changes in temperature: a multi-scale attribution analysis. Clim. Dyn., 34, 1139–1156.CrossRefGoogle Scholar

Christidis, N. et al. (2012). Human activity and anomalously warm seasons in Europe. International Journal of Climatology, 32, 225–239.CrossRefGoogle Scholar

Christidis, N. et al. (2013). A new HadGEM3-A-based system for attribution of weather- and climate-related extreme events. J. Climate, 26, 2756–2783.CrossRefGoogle Scholar

Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. London: Springer.CrossRefGoogle Scholar

Dole, R. et al. (2011). Was there a basis for anticipating the 2010 Russian heat wave? Geophysical Research Letters, 38, L06702.CrossRefGoogle Scholar

Environment Agency (2001). Lessons Learned: Autumn 2000 Floods. Bristol, UK.Google Scholar

Fouillet, A. et al. (2006). Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health, 80, 16–24.CrossRefGoogle Scholar

Gates, W. L. et al. (1999). An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 29–55.2.0.CO;2>CrossRefGoogle Scholar

Ghan, S. J. et al. (2012). Toward a minimal representation of aerosols in climate models: comparative decomposition of aerosol direct, semidirect, and indirect radiative forcing. Journal of Climate, 25, 6461–6476.CrossRefGoogle Scholar

Hémon, D. and Jougla, E. (2003). Surmortalité liée à la canicule d'août 2003: rapport d'étape. Rapport Remis Au Ministre De La Santé, De La Famille Et Des Personnes Handicapées Le 25 Septembre 2003, 59 pp.Google Scholar

Huggel, C., Stone, D. A., Auffhammer, M., and Hansen, G. (2013). Loss and damage attribution. Nature Climate Change, 3, 694–696.CrossRefGoogle Scholar

Kay, A. L., Crooks, S. M., Pall, P., and Stone, D. A. (2011). Attribution of Autumn/Winter 2000 flood risk in England to anthropogenic climate change: a catchment-based study. J. Hydrology, 406, 97–112.CrossRefGoogle Scholar

Kinter, J. and Folland, C. (2011). The International CLIVAR Climate of the 20th Century Project: Report of the Fifth Workshop. CLIVAR Exchanges, 16, 39–42.Google Scholar

Lorenz, E. N. (1982). Atmospheric predictability experiments with a large numerical-model. Tellus, 34, 505–513.CrossRefGoogle Scholar

Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M., and Wanner, H (2004). European seasonal and annual temperature variability, trends, and extremes since 1500. Science, 303, 1499–1503.CrossRefGoogle ScholarPubMed

Marsh, T. J. and Dale, M. (2002). The UK floods of 2000–2001: a hydrometeorological appraisal. J. Chart. Inst. Wat. Environ. Mgmt., 16, 180–188.CrossRefGoogle Scholar

Massey, N. et al. (2006). Data access and analysis with distributed federated data servers in climateprediction.net. Adv. Geosci., 8, 49–56.CrossRefGoogle Scholar

Mastrandrea, M. D. et al. (2010). Guidance note for lead authors of the IPCC Fifth Assessment Report on consistent treatment of uncertainties. Intergovernmental Panel on Climate Change (IPCC). Available at <http://www.ipcc.ch>..>Google Scholar

Min, S.-K., Zhang, X. B., Zwiers, F. W., Friederichs, P., and Hense, A. (2009). Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations. Climate Dyn., 32, 95–111.CrossRefGoogle Scholar

Min, S.-K., Zhang, X. B., Zwiers, F. W., and Hegerl, G. C. (2011). Human contribution to more-intense precipitation extremes. Nature, 470, 378–381.CrossRefGoogle ScholarPubMed

Min, S.-K., Zhang, X. B., Zwiers, F. W., Shiogama, H., Tung, Y.-S., and Wehner, M. F. (2013). Multi-model detection and attribution of extreme temperature changes. J. Clim., 26, 7430–7451.CrossRefGoogle Scholar

Neale, R. B. et al. (2010). Description of the NCAR Community Atmosphere Model (CAM 4.0). NCAR Technical Note, National Center of Atmospheric Research, TN-485+STR.Google Scholar

Otto, F. E. L., Massey, N., van Oldenborgh, G. J., Jones, R. G., and Allen, M. R. (2012). Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophysical Research Letters, 39, L04702.CrossRefGoogle Scholar

Pall, P., et al. (2011). Anthropogenic greenhouse gas contribution to UK autumn flood risk. Nature, 470, 382–385.CrossRefGoogle Scholar

Peterson, T. C., Stott, P. A., and Herring, S., eds. (2012). Explaining extreme events of 2011 from a climate perspective. Bull. Amer. Meteor. Soc., 93, 1041–1067.CrossRefGoogle Scholar

Peterson, T. C., Hoerling, M. P., Stott, P. A., and Herring, S., eds. (2013). Explaining extreme events of 2012 from a climate perspective. Bull. Amer. Meteor. Soc., 94(9), S1–S74.CrossRefGoogle Scholar

Rahmstorf, S., and Coumou, D. (2011). Increase of extreme events in a warming world. Proceedings of the National Academy of Sciences of the United States of America, 108, 17905–17909.CrossRefGoogle Scholar

Reichenmiller, P., Spiegel, A., Bresch, D., and Schnarwiler, R. (2010). Weathering Climate Change: Insurance Solutions for More Resilient Communities, ed. Baur, E.. Swiss Reinsurance Company Ltd., Zurich.Google Scholar

Robine, J.-M., Cheung, S. L. K., Le Roy, S. et al. (2008). Death toll exceeded 70,000 in Europe during the summer of 2003. Comptes Rendus Biologies, 331, 171–178.CrossRefGoogle Scholar

Schär, C. et al. (2004). The role of increasing temperature variability in European summer heatwaves. Nature, 427, 332–336.CrossRefGoogle ScholarPubMed

Shiogama, H. et al. (2013). An event attribution of the 2010 drought in the South Amazon region using the MIROC5 model. Atmospheric Science Letters, 14, 170–175.CrossRefGoogle Scholar

Stone, D. A. and Allen, M. R. (2005). The end-to-end attribution problem: from emissions to impacts. Clim. Change, 71, 303–318.CrossRefGoogle Scholar

Stone, D. A. et al. (2009). The detection and attribution of human influence on climate. Annu. Rev. Env. Resour., 34, 1–16.CrossRefGoogle Scholar

Stone, D. A., Paciorek, C. J., Prabhat, Pall, P., and Wehner, M. F. (2013). Inferring the anthropogenic contribution to local temperature extremes. Proceedings of the National Academy of Sciences, 110, E1543.CrossRefGoogle ScholarPubMed

Stott, P. A., Stone, D. A., and Allen, M. R. (2004). Human contribution to the European heatwave of 2003. Nature, 432, 610–614.CrossRefGoogle Scholar

Stott, P. A. et al. (2013). Attribution of weather and climate-related events. In Climate Science for Serving Society: Research, Modeling and Prediction Priorities, eds. Asrar, G. R. and Hurrell, J. W.. Springer, pp. 307–337.CrossRefGoogle Scholar

Uppala, S. M. et al. (2005). The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2961–3012.CrossRefGoogle Scholar

Wehner, M. F., Smith, R. L., Bala, G., and Duffy, P. (2010). The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Climate Dynamics, 34, 241–247.CrossRefGoogle Scholar

Wergen, G., Hense, A., and Krug, J. (2014). Record occurrence and record values in daily and monthly temperatures. Clim. Dyn., 42, 1275–1289.CrossRefGoogle Scholar

Wolski, P., Stone, D. A., Tadross, M., Wehner, M. F., and Hewitson, B. (2014). Attribution of floods in the Okavango Basin, Southern Africa. Journal of Hydrology, 511, 350–358.CrossRefGoogle Scholar

Alexander, L. V. and Arblaster, J. M. (2009). Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int. J. Climatol., 29, 417–435.CrossRefGoogle Scholar

Alexander, L. V., et al., (2006): Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.CrossRefGoogle Scholar

Allen, M. R. and Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224–232.CrossRefGoogle ScholarPubMed

Allen, M. R. and Stott, P. A. (2003). Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Clim. Dyn., 21, 477–491.CrossRefGoogle Scholar

Arora, V. K., Scinocca, J. F., Boer, G. J., et al. (2011). Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett., 38, L05805, doi:10.1029/2010GL046270.CrossRefGoogle Scholar

Bernard, E., Naveau, P., Vrac, M., and Mestre, O. (2013). Clustering of maxima: spatial dependencies among heavy rainfall in France. J. Climate, 26, 7929–7937. doi: http://dx.doi.org/10.1175/JCLI-D-12-00836.1CrossRefGoogle Scholar

Bindoff, N.L., Stott, P.A., AchutaRao, K.M., et al., (2013). Detection and attribution of climate change: from global to regional. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.-K., et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952.Google Scholar

Caesar, J., Alexander, L., and Vose, R., 2006. Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res. Atmos., 111, D05101.CrossRefGoogle Scholar

Christidis, N., Stott, P. A., and Brown, S. J. (2011). The role of human activity in the recent warming of extremely warm daytime temperatures. J Climate, 24, 1922–1930.CrossRefGoogle Scholar

Coles, S. (2001). An Introduction to the Statistical Modeling of Extreme Values. Springer, London, 208 pp. ISBN ISBN 1-85233-459-2.CrossRefGoogle Scholar

Cox, D. R. and Hinkley, D. V. (1974): Theoretical Statistics. Chapman and Hall, 511 pp.CrossRefGoogle Scholar

Cox, D. R. and Lewis, P. A. W. (1966): The Statistical Analysis of Series of Events. John Wiley and Sons, 285 pp.CrossRefGoogle Scholar

Donat, M. G., et al., (2013). Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. Journal of Geophysical Research: Atmospheres, doi:10.1002/jgrd.50150.CrossRefGoogle Scholar

El Adlouni, S., Ouarda, T. B. M. J., Zhang, X., Roy, R., and Bobe´e, B. (2007). Generalized maximum likelihood estimators for the nonstationary generalized extreme value model, Water Resour. Res., 43, W03410, doi:10.1029/2005WR004545.CrossRefGoogle Scholar

Fisher, R. A. and Tippett, L. H. C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. Proc. Cambridge Philos. Soc., 24, 180–190.CrossRefGoogle Scholar

Frei, C. and Schär, C. (2001). Detection probability of trends in rare events: theory and application to heavy precipitation in the Alpine region. J. Climate, 14, 1568–1584.2.0.CO;2>CrossRefGoogle Scholar

Frich, P., Alexander, L. V., Della-Marta, P., et al. (2002). Observed coherent changes in climatic extremes during the second half of the 20th century, Climate Research, 19, 193–212.CrossRefGoogle Scholar

Fowler, H. J. and Wilby, R. L. (2010). Detecting changes in seasonal precipitation extremes using regional climate model projections: Implications for managing fluvial flood risk. Water Resour. Res., 46, W03525.CrossRefGoogle Scholar

Gnedenko, B. V. (1943). Sur la distribution limite du terme maximum d'une se´rie ale´atoire (Limiting distribution of maximum values of random series). Ann. Math., 44, 423–453.CrossRefGoogle Scholar

Gumbel, E. J. (1958): Statistics of Extremes. Columbia University Press, 375 pp.CrossRefGoogle Scholar

Hanlon, H., Hegerl, G. C., Tett, S. F. B., and Smith, D. M. (2012). Can a decadal forecasting system predict temperature extreme indices? J Climate, doi 10.1175/JCLI-D-12-00512.1Google Scholar

Hartmann, D.L., Klein Tank, A.M.G., Rusticucci, M., et al., (2013). Observations: atmosphere and surface. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.-K., et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 158–254.Google Scholar

Hasselmann, K. (1979). On the signal-to-noise problem in atmospheric response studies. In Meteorology of Tropical Oceans [Shaw, D. B. (ed.)]. Royal Meteorological Society, Bracknell, UK, pp. 251–259.Google Scholar

Hegerl, G. C., Storch, H. v., Hasselmann, K., et al. (1996). Detecting greenhouse gas induced Climate Change with an optimal fingerprint method. J. Climate, 9, 2281–2306.2.0.CO;2>CrossRefGoogle Scholar

Hegerl, G.C., Hasselmann, K., Cubasch, U., et al. (1997). Multi-fingerprint detection and attribution of greenhouse-gas and aerosol-forced climate change. Clim. Dyn., 13, 613–634.CrossRefGoogle Scholar

Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., et al. (2010). Good practice guidance paper on detection and attribution related to anthropogenic climate change. In Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change, Stocker, T. F., et al., eds., IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland.Google Scholar

Hegerl, G. C. and Zwiers, F. W. (2011). Use of models in detection and attribution of climate change. WIRES: Climate Change, 2, 570–591.Google Scholar

Hosking, J. R. M. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. J. R. Stat. Soc., 52, 105–12.Google Scholar

IPCC (2012). Summary for policymakers. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., Barros, V., Stocker, T.F., et al. (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 1–19.CrossRefGoogle Scholar

Katz, R., Parlange, M., and Naveau, P. (2002). Extremes in hydrology. Adv. Water Resour., 25, 1287–1304.CrossRefGoogle Scholar

Keim, B. D. and Cruise, J. F. (1998). A technique to measure trends in the frequency of discrete random events, J. Climate, 11, 848–854.2.0.CO;2>CrossRefGoogle Scholar

Klok, E. J. and Tank, A., 2009. Updated and extended European dataset of daily climate observations. Int. J. Climatol., 29, 1182–1191.CrossRefGoogle Scholar

Kunkel, K. E., Andsager, K., and Easterling, D. R. (1999). Long-term trends in extreme precipitation events over coterminous United States and Canada. J. Climate, 12, 2515–2527.2.0.CO;2>CrossRefGoogle Scholar

Kharin, V. V. and Zwiers, F. W. (2000). Changes in the extremes in an ensemble of transient climate simulation with a coupled atmosphere–ocean GCM. J. Climate, 13, 3760–3788.2.0.CO;2>CrossRefGoogle Scholar

Kharin, V. V. and Zwiers, F. W. (2005). Estimating extremes in transient climate change simulations. J. Climate, 18, 1156–1173.CrossRefGoogle Scholar

Kharin, V. V., Zwiers, F. W., Zhang, X., and Hegerl, G. C. (2007). Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Climate, 20, 1419–1444.CrossRefGoogle Scholar

Kharin, V. V., Zwiers, F. W., Zhang, X., and Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim. Change, doi:10.1007/ s10584-013-0705-8.Google Scholar

Meehl, G. A., Arblaster, J. M., and Tebaldi, C. (2007). Contributions of natural and anthropogenic forcing to changes in temperature extremes over the U.S. Geophys. Res. Lett., 34, L19709.CrossRefGoogle Scholar

Min, S.-K., Zhang, X., Zwiers, F. W., and Hegerl, G. C. (2011). Human contribution to more-intense precipitation extremes. Nature, 470, 378–381. doi: 10.1038/nature09763CrossRefGoogle ScholarPubMed

Min, S.-K., Zhang, X., Zwiers, F., et al. (2013). Multi-model detection and attribution of extreme temperature changes. Journal of Climate, doi:10.1175/JCLI-D-12-00551.w.CrossRefGoogle Scholar

Morak, S., Hegerl, G. C., and Christidis, N. (2013). Detectable changes in the frequency of temperature extremes. Journal of Climate, 26, 1561–1574.CrossRefGoogle Scholar

Morak, S., Hegerl, G. C., and Kenyon, J. (2011). Detectable regional changes in the number of warm nights. Geophysical Research Letters, 38, L17703.CrossRefGoogle Scholar

Mueller, B and Seneviratne, S. I. (2012). Hot days induced by precipitation deficits at the global scale. Proc Natl Acad Sci USA, 109, 12398–12403, doi: 10.1073/pnas.1204330109.CrossRefGoogle ScholarPubMed

Ribes, A, Planton, S., and Terray, L. (2013). Application of regularized optimal fingerprinting to attribution. Part I: method, properties and idealized analysis. Clim Dyn, doi:10.1007/s00382-013-1735-7CrossRefGoogle Scholar

Seneviratne, S.I., Nicholls, N., Easterling, D., et al., (2012). Changes in climate extremes and their impacts on the natural physical environment. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., Barros, V., Stocker, T.F., et al. (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109–230.Google Scholar

Shepard, D. (1968). A two-dimensional interpolation function for irregularly spaced data, paper presented at 23rd National Conference, Assoc. for Comput. Mach, New York.Google Scholar

Sillmann, J., Kallache, M., Croci-Maspoli, M., and Katz, R. W., (2011). Extreme cold winter temperatures in Europe under the influence of North Atlantic atmospheric blocking. Journal of Climate, 24, 5899–5913.CrossRefGoogle Scholar

Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., and Bronaugh, D. (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos., 118, 2473–2493, doi:10.1002/jgrd.50188CrossRefGoogle Scholar

Sillmann, J., Donat, M., Fyfe, J. C., and Zwiers, F. W. (2014). Observed and simulated temperature extremes during the recent warming hiatus. Environmental Research Letters, 9(6), 8 pp.CrossRefGoogle Scholar

Smith, R. L. (1989). Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone (with discussion). Stat. Sci., 4, 367–393.Google Scholar

Trenberth, K. E., Jones, P. D., Ambenje, P., et al. (2007). Observations: surface and atmospheric climate change. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., Qin, D., Manning, M., et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Google Scholar

Wang, X. L. and Swail, V. R. (2004). Historical and possible future changes of wave heights in northern hemisphere oceans. In: Atmosphere Ocean Interactions, 2, W. Perrie (ed.). Wessex Institute of Technology Press, Southampton, UK.Google Scholar

Wang, J. and Zhang, X. (2008). Downscaling and projection of winter extreme daily precipitation over North America. J. Climate, 21, 923–937.CrossRefGoogle Scholar

Wehner, M. F. (2013). Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn, 40, 59–80. doi: 10.1007/s00382-012-1393-1.CrossRefGoogle Scholar

Wehner, M. F., Smith, R. L., and Pala Duffy, T. L. (2010). The effect of horizontal resolution on simulation of very extreme precipitation events in a global atmospheric model. Clim Dyn 34, 241–247. doi: 10.1007/s00382-009-0656-yCrossRefGoogle Scholar

Wen, H. Q., Zhang, X., Xu, Y., and Wang, B. (2013). Detecting human influence on temperature extremes in China. Geographical Research Letter, doi: 10.1002/grl.50285CrossRefGoogle Scholar

Westra, S., Alexander, L. V., and Zwiers, F. W. (2013). Global increasing trends in annual maximum daily precipitation. J. Climate, doi:10.1175/JCLI-D-12-00502.1.CrossRefGoogle Scholar

Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W., and Cook, E. R. (2010). A 1200-year perspective on 21st century drought in southwestern North America. Proc Natl Acad Sci, USA, 107, 21283–21288.CrossRefGoogle Scholar

Woodhouse, C.A. and Overpeck, J. T. (1998.: 2000 years of drought variability in the central United States. Bull Am Meteorol Soc, 79, 2693–2714.2.0.CO;2>CrossRefGoogle Scholar

Zhang, X., Alexander, L. V., Hegerl, G. C., et al. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip Rev Clim Change, 851–870. doi: 10.1002/wcc.147CrossRefGoogle Scholar

Zhang, X., Hogg, W. D., and Mekis, E. (2001). Spatial and temporal characteristics of heavy precipitation events over Canada. J. Climate, 14, 1923–1936.2.0.CO;2>CrossRefGoogle Scholar

Zhang, X., Wan, H., Zwiers, F. W., Hegerl, G. C., and Min, S.-K., (2013). Attributing intensification of precipitation extremes to human influence, Geophys. Res. Lett., 40, 5252–5257, doi:10.1002/grl.51010.CrossRefGoogle Scholar

Zhang, X., Wang, J., Zwiers, F. W., and Groisman, P. Y. (2010). The influence of large scale climate variability on winter maximum daily precipitation over North America. J. Climate, 23, 2902–2915.CrossRefGoogle Scholar

Zhang, X. and Zwiers, F. W. (2013). Statistical indices for diagnosing and detecting changes in extremes. In Hydrologic Extremes in a Changing Climate: Detection, Analysis & Uncertainty (Eds. Sorooshian, et al.), Springer-Verlag.Google Scholar

Zhang, X., Zwiers, F. W., and Li, G. (2004). Monte Carlo experiments on the detection of trends in extreme values. J. Climate, 17, 1945–1952.2.0.CO;2>CrossRefGoogle Scholar

Zwiers, F. W. and Kharin, V. V. (1998). Changes in the extremes of climate simulated by CCC GCM2 under CO2 doubling. J. Climate, 11, 2200–2222.2.0.CO;2>CrossRefGoogle Scholar

Zwiers, F. W., Zhang, X., and Feng, Y. (2011). Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Climate, 24, 881–892.CrossRefGoogle Scholar