Articles | Volume 2, issue 1
https://doi.org/10.5194/ascmo-2-79-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-2-79-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions
Whitney K. Huang
CORRESPONDING AUTHOR
Department of Statistics, Purdue University, West Lafayette, IN
47907, USA
Michael L. Stein
Department of Statistics, University of Chicago, Chicago, IL
60637, USA
David J. McInerney
School of Civil, Environmental and Mining Engineering,
University of Adelaide, Adelaide, South Australia, 5005, Australia
Shanshan Sun
Department of the Geophysical Sciences, University of Chicago,
Chicago, IL 60637, USA
Elisabeth J. Moyer
Department of the Geophysical Sciences, University of Chicago,
Chicago, IL 60637, USA
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Atmos. Meas. Tech., 19, 1147–1163, https://doi.org/10.5194/amt-19-1147-2026, https://doi.org/10.5194/amt-19-1147-2026, 2026
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Water molecules comes in several varieties, of which H216O is the most common. These varieties behave differently enough under freezing to create strong changes in the ratio of heavy to light water molecules. Here we compare observations of these ratios from satellites and high-altitude airborne instruments. These observations provide information about how air reaches the upper parts of the atmosphere, so it is important to reconcile difference between different modes of observations.
Cristina Prieto, Dmitri Kavetski, Fabrizio Fenicia, James Kirchner, David McInerney, Mark Thyer, and César Álvarez
EGUsphere, https://doi.org/10.5194/egusphere-2026-483, https://doi.org/10.5194/egusphere-2026-483, 2026
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Hourly streamflow data are increasingly available and can improve streamflow predictions. We tested simple ways to describe uncertainty by transforming flow values and by accounting for how errors persist from hour to hour, using seven catchments in Spain, Switzerland and the United States. Simple transformations and short-term error memory improve the reliability of probabilistic predictions and help combine hourly results into longer time scales for practical operational contexts.
Benjamin W. Clouser, Laszlo C. Sarkozy, Clare E. Singer, Carly C. KleinStern, Adrien Desmoulin, Dylan Gaeta, Sergey Khaykin, Stephen Gabbard, Stephen Shertz, and Elisabeth J. Moyer
Atmos. Meas. Tech., 18, 6465–6491, https://doi.org/10.5194/amt-18-6465-2025, https://doi.org/10.5194/amt-18-6465-2025, 2025
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The water molecule comes in several different varieties, which are nearly indistinguishable in daily life. However, slight differences between the water molecule types can be exploited to achieve better scientific understanding of parts of Earth's atmosphere. In this work we describe the design, construction, and operation of an instrument meant to measure these molecules aboard research aircraft up to altitudes of 20 km.
Paul Konopka, Christian Rolf, Marc von Hobe, Sergey M. Khaykin, Benjamin Clouser, Elisabeth Moyer, Fabrizio Ravegnani, Francesco D'Amato, Silvia Viciani, Nicole Spelten, Armin Afchine, Martina Krämer, Fred Stroh, and Felix Ploeger
Atmos. Chem. Phys., 23, 12935–12947, https://doi.org/10.5194/acp-23-12935-2023, https://doi.org/10.5194/acp-23-12935-2023, 2023
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We studied water vapor in a critical region of the atmosphere, the Asian summer monsoon anticyclone, using rare in situ observations. Our study shows that extremely high water vapor values observed in the stratosphere within the Asian monsoon anticyclone still undergo significant freeze-drying and that water vapor concentrations set by the Lagrangian dry point are a better proxy for the stratospheric water vapor budget than rare observations of enhanced water mixing ratios.
Kara D. Lamb, Jerry Y. Harrington, Benjamin W. Clouser, Elisabeth J. Moyer, Laszlo Sarkozy, Volker Ebert, Ottmar Möhler, and Harald Saathoff
Atmos. Chem. Phys., 23, 6043–6064, https://doi.org/10.5194/acp-23-6043-2023, https://doi.org/10.5194/acp-23-6043-2023, 2023
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This study investigates how ice grows directly from vapor in cirrus clouds by comparing observations of populations of ice crystals growing in a cloud chamber against models developed in the context of single-crystal laboratory studies. We demonstrate that previous discrepancies between different experimental measurements do not necessarily point to different physical interpretations but are rather due to assumptions that were made in terms of how experiments were modeled in previous studies.
Richard Laugesen, Mark Thyer, David McInerney, and Dmitri Kavetski
Hydrol. Earth Syst. Sci., 27, 873–893, https://doi.org/10.5194/hess-27-873-2023, https://doi.org/10.5194/hess-27-873-2023, 2023
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Forecasts may be valuable for user decisions, but current practice to quantify it has critical limitations. This study introduces RUV (relative utility value, a new metric that can be tailored to specific decisions and decision-makers. It illustrates how critical this decision context is when evaluating forecast value. This study paves the way for agencies to tailor the evaluation of their services to customer decisions and researchers to study model improvements through the lens of user impact.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
Clare E. Singer, Benjamin W. Clouser, Sergey M. Khaykin, Martina Krämer, Francesco Cairo, Thomas Peter, Alexey Lykov, Christian Rolf, Nicole Spelten, Armin Afchine, Simone Brunamonti, and Elisabeth J. Moyer
Atmos. Meas. Tech., 15, 4767–4783, https://doi.org/10.5194/amt-15-4767-2022, https://doi.org/10.5194/amt-15-4767-2022, 2022
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In situ measurements of water vapor in the upper troposphere are necessary to study cloud formation and hydration of the stratosphere but challenging due to cold–dry conditions. We compare measurements from three water vapor instruments from the StratoClim campaign in 2017. In clear sky (clouds), point-by-point differences were <1.5±8 % (<1±8 %). This excellent agreement allows detection of fine-scale structures required to understand the impact of convection on stratospheric water vapor.
Sergey M. Khaykin, Elizabeth Moyer, Martina Krämer, Benjamin Clouser, Silvia Bucci, Bernard Legras, Alexey Lykov, Armin Afchine, Francesco Cairo, Ivan Formanyuk, Valentin Mitev, Renaud Matthey, Christian Rolf, Clare E. Singer, Nicole Spelten, Vasiliy Volkov, Vladimir Yushkov, and Fred Stroh
Atmos. Chem. Phys., 22, 3169–3189, https://doi.org/10.5194/acp-22-3169-2022, https://doi.org/10.5194/acp-22-3169-2022, 2022
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The Asian monsoon anticyclone is the key contributor to the global annual maximum in lower stratospheric water vapour. We investigate the impact of deep convection on the lower stratospheric water using a unique set of observations aboard the high-altitude M55-Geophysica aircraft deployed in Nepal in summer 2017 within the EU StratoClim project. We find that convective plumes of wet air can persist within the Asian anticyclone for weeks, thereby enhancing the occurrence of high-level clouds.
Cited articles
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Ambenje, P., Kumar, K., Rupa, Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguliar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006.
Ballester, J., Giorgi, F., and Rodó, X.: Changes in European temperature extremes can be predicted from changes in PDF central statistics, Climatic Change, 98, 277–284, 2010.
Barbosa, S. M., Scotto, M. G., and Alonso, A. M.: Summarising changes in air temperature over Central Europe by quantile regression and clustering, Nat. Hazards Earth Syst. Sci., 11, 3227–3233, https://doi.org/10.5194/nhess-11-3227-2011, 2011.
Beirlant, J., Goegebeur, Y., Segers, J., and Teugels, J.: Statistics of Extremes: Theory and Applications, John Wiley & Sons, New York, ISBN: 0471976474, 2004.
Castruccio, S., McInerney, D. J., Stein, M. L., Liu Crouch, F., Jacob, R. L., and Moyer, E. J.: Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*, J. Climate, 27, 1829–1844, 2014.
Chavez-Demoulin, V. and Davison, A. C.: Generalized additive modelling of sample extremes, J. Roy. Stat. Soc. C-App., 54, 207–222, 2005.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer, London, ISBN: 1852334592, 2001.
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.: The community climate system model version 3 (CCSM3), J. Climate, 19, 2122–2143, 2006.
Cooley, D. and Sain, S. R.: Spatial hierarchical modeling of precipitation extremes from a regional climate model, J. Agr. Biol. Envir. St., 15, 381–402, 2010.
Cooley, D., Nychka, D., and Naveau, P.: Bayesian spatial modeling of extreme precipitation return levels, J. Am. Stat. Assoc., 102, 824–840, 2007.
Craigmile, P. F. and Guttorp, P.: Can a regional climate model reproduce observed extreme temperatures?, Statistica, 73, 103–122, 2013.
Davison, A. C. and Smith, R. L.: Models for exceedances over high thresholds (with discussion), J. Roy. Stat. Soc. B, 52, 393–442, 1990.
de Haan, L. and Ferreira, A.: Extreme Value Theory: an Introduction, Springer, New York, ISBN: 0387239464, 2006.
de Vries, H., Haarsma, R. J., and Hazeleger, W.: Western European cold spells in current and future climate, Geophys. Res. Lett., 39, L04706, https://doi.org/10.1029/2011GL050665, 2012.
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and Mearns, L. O.: Climate extremes: observations, modeling, and impacts, Science, 289, 2068–2074, 2000.
Efron, B.: Bootstrap methods: another look at the jackknife, Ann. Stat., 7, 1–26, 1979.
Einmahl, J. H., Haan, L., and Zhou, C.: Statistics of heteroscedastic extremes, J. Roy. Stat. Soc. B, 78, 31–51, 2016.
Fisher, R. A. and Tippett, L. H. C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample, Math. Proc. Cambridge, 24, 180–190, 1928.
Frías, M. D., Mínguez, R., Gutiérrez, J. M., and Méndez, F. J.: Future regional projections of extreme temperatures in Europe: a nonstationary seasonal approach, Climatic Change, 113, 371–392, 2012.
Frich, P., Alexander, L., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A., and Peterson, T.: Observed coherent changes in climatic extremes during the second half of the twentieth century, Climate Research, 19, 193–212, 2002.
Fuentes, M., Henry, J., and Reich, B.: Nonparametric spatial models for extremes: application to extreme temperature data, Extremes, 16, 75–101, 2013.
Gilleland, E. and Katz, R. W.: Analyzing seasonal to interannual extreme weather and climate variability with the extremes toolkit, in: 18th Conference on Climate Variability and Change, 86th American Meteorological Society (AMS) Annual Meeting, vol. 29, Citeseer, 29 January 2006.
Gnedenko, B.: Sur la distribution limite du terme maximum d'une serie aleatoire, Ann. Math., 44, 423–453, 1943.
Gumbel, E. J.: Statistics of extremes, Columbia Univ. Press, New York, ISBN: 0231021909, 1958.
Heaton, M. J., Katzfuss, M., Ramachandar, S., Pedings, K., Gilleland, E., Mannshardt-Shamseldin, E., and Smith, R. L.: Spatio-temporal models for large-scale indicators of extreme weather, Environmetrics, 22, 294–303, 2011.
Holmes, C. R., Woollings, T., Hawkins, E., and de Vries, H.: Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection, J. Climate, 29, 2221–2237, 2016.
Hosking, J., Wallis, J. R., and Wood, E. F.: Estimation of the generalized extreme-value distribution by the method of probability-weighted moments, Technometrics, 27, 251–261, 1985.
Hsing, T.: On tail index estimation using dependent data, Ann. Stat., 19, 1547–1569, 1991.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013.
Katz, R. W., Parlange, M. B., and Naveau, P.: Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287–1304, 2002.
Kharin, V. V. and Zwiers, F. W.: Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere-ocean GCM, J. Climate, 13, 3760–3788, 2000.
Kharin, V. V. and Zwiers, F. W.: Estimating extremes in transient climate change simulations, J. Climate, 18, 1156–1173, 2005.
Kharin, V. V., Zwiers, F. W., Zhang, X., and Hegerl, G. C.: Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations, J. Climate, 20, 1419–1444, 2007.
Kharin, V. V., Zwiers, F., Zhang, X., and Wehner, M.: Changes in temperature and precipitation extremes in the CMIP5 ensemble, Climatic Change, 119, 345–357, 2013.
Kunkel, K. E., Pielke Jr, R. A., and Changnon, S. A.: Temporal fluctuations in weather and climate extremes that cause economic and human health impacts: A review, B. Am. Meteorol. Soc., 80, 1077–1098, 1999.
Leadbetter, M. R., Lindgren, G., and Rootzén, H.: Extremes and Related Properties of Random Sequences and Processes, Springer, New York, ISBN: 0387907319, 1983.
Lee, J., Li, S., and Lund, R.: Trends in extreme US temperatures, J. Climate, 27, 4209–4225, 2014.
Leeds, W. B., Moyer, E. J., and Stein, M. L.: Simulation of future climate under changing temporal covariance structures, Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, 2015.
Naveau, P., Guillou, A., and Rietsch, T.: A non-parametric entropy-based approach to detect changes in climate extremes, J. Roy. Stat. Soc. B, 76, 861–884, 2014.
NOAA: Billion-Dollar Weather and Climate Disasters: Overview, available at: https://www.ncdc.noaa.gov/billions/ (last access: 16 June 2016), 2015.
Parey, S., Dacunha-Castelle, D., and Hoang, T. H.: Mean and variance evolutions of the hot and cold temperatures in Europe, Clim. Dynam., 34, 345–359, 2010.
Parey, S., Hoang, T. T. H., and Dacunha-Castelle, D.: The importance of mean and variance in predicting changes in temperature extremes, J. Geophys. Res.-Atmos., 118, 8285–8296, 2013.
Pickands III, J.: Statistical inference using extreme order statistics, Ann. Stat., 3, 119–131, 1975.
Politis, D. N. and Romano, J. P.: A circular block-resampling procedure for stationary data, in: Exploring the limits of bootstrap, edited by: LePage, R. and Billard, L., 263–270, John Wiley, New York, 1992.
Resnick, S. I.: Extreme Values, Regular Variation, and Point Processes, Springer, New York, ISBN: 9780387759531, 1987.
Resnick, S. I.: Heavy-Tail Phenomena: Probabilistic and Statistical Modeling, Springer, New York, ISBN: 9780387242729, 2007.
Shaby, B. A. and Reich, B. J.: Bayesian spatial extreme value analysis to assess the changing risk of concurrent high temperatures across large portions of European cropland, Environmetrics, 23, 638–648, 2012.
Shi, P., Wang, B., Ayres, M. P., Ge, F., Zhong, L., and Li, B.-L.: Influence of temperature on the northern distribution limits of Scirpophaga incertulas Walker (Lepidoptera: Pyralidae) in China, J. Therm. Biol., 37, 130–137, 2012.
Smith, A. B. and Katz, R. W.: US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases, Nat. Hazards, 67, 387–410, 2013.
Smith, R. L.: Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone, Stat. Sci., 4, 367–377, 1989.
Stephenson, A. and Heffernan, J.: ismev: An Introduction to Statistical Modeling of Extreme Values, Original S functions written by Janet E. Heffernan with R port and R documentation provided by Alec G. Stephenson., R package version 1.40, 2014.
Sterl, A., Severijns, C., Dijkstra, H., Hazeleger, W., Jan van Oldenborgh, G., van den Broeke, M., Burgers, G., van den Hurk, B., Jan van Leeuwen, P., and van Velthoven, P.: When can we expect extremely high surface temperatures?, Geophys. Res. Lett., 35, L14703, https://doi.org/10.1029/2008GL034071, 2008.
Tebaldi, C., Hayhoe, K., Arblaster, J. M., and Meehl, G. A.: Going to the extremes, Climatic change, 79, 185–211, 2006.
Wang, J., Han, Y., Stein, M. L., Kotamarthi, V. R., and Huang, W. K.: Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model, Clim. Dynam., https://doi.org/10.1007/s00382-016-3000-3, 2016.
Weed, A. S., Ayres, M. P., and Hicke, J. A.: Consequences of climate change for biotic disturbances in North American forests, Ecol. Monogr., 83, 441–470, 2013.
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global increasing trends in annual maximum daily precipitation, J. Climate, 26, 3904–3918, 2013.
Yeager, S. G., Shields, C. A., Large, W. G., and Hack, J. J.: The low-resolution CCSM3, J. Climate, 19, 2545–2566, 2006.
Yee, T. W. and Stephenson, A. G.: Vector generalized linear and additive extreme value models, Extremes, 10, 1–19, 2007.
Zhang, X. and Zwiers, F. W.: Statistical indices for the diagnosing and detecting changes in extremes, in: Extremes in a Changing Climate, edited by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S., 1–14, Springer, Dordrecht, 2013.
Zwiers, F. W. and Kharin, V. V.: Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling, J. Climate, 11, 2200–2222, 1998.