Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-09T06:03:48.888Z Has data issue: false hasContentIssue false

4 - Observed and projected changes in temperature and precipitation extremes

from Part I - Diagnostics and prediction of high-impact weather

Published online by Cambridge University Press:  05 March 2016

By
Xuebin Zhang  and
Francis Zwiers
Edited by
Jianping Li ,
Richard Swinbank ,
Richard Grotjahn  and
Hans Volkert
Jianping Li
Affiliation:
Beijing Normal University
Richard Swinbank
Affiliation:
Met Office, Exeter
Richard Grotjahn
Affiliation:
University of California, Davis
Hans Volkert
Affiliation:
Deutsche Zentrum für Luft- und Raumfahrt eV (DLR)
Get access
Type
Chapter
Information
Dynamics and Predictability of Large-Scale, High-Impact Weather and Climate Events , pp. 47 - 66
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×