Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-45-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-45-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Changes in the distribution of annual maximum temperatures in Europe
Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
Gabriele C. Hegerl
School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom
Ioannis Papastathopoulos
School of Mathematics and Maxwell Institute, University of Edinburgh, Edinburgh, United Kingdom
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
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Mathew Williams, David T. Milodowski, T. Luke Smallman, Kyle G. Dexter, Gabi C. Hegerl, Iain M. McNicol, Michael O'Sullivan, Carla M. Roesch, Casey M. Ryan, Stephen Sitch, and Aude Valade
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Southern African woodlands are important in both regional and global carbon cycles. A new carbon analysis created by combining satellite data with ecosystem modelling shows that the region has a neutral C balance overall but with important spatial variations. Patterns of biomass and C balance across the region are the outcome of climate controls on production and vegetation–fire interactions, which determine the mortality of vegetation and spatial variations in vegetation function.
Lauren R. Marshall, Anja Schmidt, Andrew P. Schurer, Nathan Luke Abraham, Lucie J. Lücke, Rob Wilson, Kevin J. Anchukaitis, Gabriele C. Hegerl, Ben Johnson, Bette L. Otto-Bliesner, Esther C. Brady, Myriam Khodri, and Kohei Yoshida
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Large volcanic eruptions have caused temperature deviations over the past 1000 years; however, climate model results and reconstructions of surface cooling using tree rings do not match. We explore this mismatch using the latest models and find a better match to tree-ring reconstructions for some eruptions. Our results show that the way in which eruptions are simulated in models matters for the comparison to tree-rings, particularly regarding the spatial spread of volcanic aerosol.
Lucie J. Lücke, Andrew P. Schurer, Matthew Toohey, Lauren R. Marshall, and Gabriele C. Hegerl
Clim. Past, 19, 959–978, https://doi.org/10.5194/cp-19-959-2023, https://doi.org/10.5194/cp-19-959-2023, 2023
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Evidence from tree rings and ice cores provides incomplete information about past volcanic eruptions and the Sun's activity. We model past climate with varying solar and volcanic scenarios and compare it to reconstructed temperature. We confirm that the Sun's influence was small and that uncertain volcanic activity can strongly influence temperature shortly after the eruption. On long timescales, independent data sources closely agree, increasing our confidence in understanding of past climate.
Jörg Franke, Michael N. Evans, Andrew Schurer, and Gabriele C. Hegerl
Clim. Past, 18, 2583–2597, https://doi.org/10.5194/cp-18-2583-2022, https://doi.org/10.5194/cp-18-2583-2022, 2022
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Detection and attribution is a statistical method to evaluate if external factors or random variability have caused climatic changes. We use for the first time a comparison of simulated and observed tree-ring width that circumvents many limitations of previous studies relying on climate reconstructions. We attribute variability in temperature-limited trees to strong volcanic eruptions and for the first time detect a spatial pattern in the growth of moisture-sensitive trees after eruptions.
Davide Zanchettin, Claudia Timmreck, Myriam Khodri, Anja Schmidt, Matthew Toohey, Manabu Abe, Slimane Bekki, Jason Cole, Shih-Wei Fang, Wuhu Feng, Gabriele Hegerl, Ben Johnson, Nicolas Lebas, Allegra N. LeGrande, Graham W. Mann, Lauren Marshall, Landon Rieger, Alan Robock, Sara Rubinetti, Kostas Tsigaridis, and Helen Weierbach
Geosci. Model Dev., 15, 2265–2292, https://doi.org/10.5194/gmd-15-2265-2022, https://doi.org/10.5194/gmd-15-2265-2022, 2022
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This paper provides metadata and first analyses of the volc-pinatubo-full experiment of CMIP6-VolMIP. Results from six Earth system models reveal significant differences in radiative flux anomalies that trace back to different implementations of volcanic forcing. Surface responses are in contrast overall consistent across models, reflecting the large spread due to internal variability. A second phase of VolMIP shall consider both aspects toward improved protocol for volc-pinatubo-full.
Cited articles
Allen, M., Dube, O., Solecki, W., Aragón-Durand, F., Cramer, W., Humphreys,
S., Kainuma, M., Kala, J., Mahowald, N., Mulugetta, Y., Perez, R., Wairiu,
M., and Zickfeld, K.: Framing and Context, in: Global Warming of
1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C
above pre-industrial levels and related global greenhouse gas emission
pathways, in the context of strengthening the global response to the threat
of climate change, sustainable development, and efforts to eradicate poverty, edited by: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla,
P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S.,
Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock,
T., Tignor, M., and Waterfield, T., World Meteorological Organization,
Geneva, Switzerland,
https://www.ipcc.ch/sr15/chapter/chapter-1/ (last access: 16 May 2023), https://doi.org/10.1017/9781009157940.003, 2018. a
Andrade, C., Leite, S. M., and Santos, J. A.: Temperature extremes in Europe: overview of their driving atmospheric patterns, Nat. Hazards Earth Syst. Sci., 12, 1671–1691, https://doi.org/10.5194/nhess-12-1671-2012, 2012. a
Auld, G., Papastathopoulos, I., and Hegerl, G.: DataAndCode.zip, figshare [data set], https://doi.org/10.6084/m9.figshare.21257217.v1, 2023. a
Banerjee, S., Carlin, B., and Gelfand, A.: Hierarchical Modeling and Analysis
for Spatial Data, Monographs on Statistical and Applied Probability, Chapman
& Hall/CRC, New York, https://doi.org/10.1201/b17115, 2004. a
Basu, R. and Samet, J.: Relation between elevated ambient temperature and
mortality: a review of the epidemiologic evidence, Epidemiol. Rev., 24,
190–202, https://doi.org/10.1093/epirev/mxf007, 2002. a
Brunsdon, C., Fotheringham, S., and Charlton, M.: Geographically weighted
regression-modelling spatial non-stationarity, J. Roy.
Stat. Soc. Ser. D, 47, 431–443,
https://doi.org/10.1111/1467-9884.00145, 1998. a
Bücher, A. and Segers, J.: On the maximum likelihood estimator for the
Generalized Extreme-Value distribution, Extremes, 20, 839–872,
https://doi.org/10.1007/s10687-017-0292-6, 2017. a
Chandler, R. E. and Bate, S.: Inference for clustered data using the
independence loglikelihood, Biometrika, 94, 167–183,
https://doi.org/10.1093/biomet/asm015, 2007. a
Charney, J., Arakawa, A., Baker, D., Bolin, Dickinson, B. R., Goody, R., Leith,
C., Stommel, H., and Wunsch, C.: Carbon Dioxide and Climate: A Scientific
Assessment, The National Academies Press, Washington, DC,
https://doi.org/10.17226/12181, 1979. a
Chavez-Demoulin, V. and Davison, A. C.: Generalized additive modelling of
sample extremes, J. Roy. Stat. Soc. Ser. C, 54, 207–222, https://doi.org/10.1111/j.1467-9876.2005.00479.x, 2005. a
Chen, S.-Y., Feng, Z., and Yi, X.: A general introduction to adjustment for
multiple comparisons, J. Thorac. Dis., 9, 1725–1729,
https://doi.org/10.21037/jtd.2017.05.34, 2017. a
Coles, S. and Dixon, M.: Likelihood-based inference for extreme value models,
Extremes, 2, 5–23, https://doi.org/10.1023/A:1009905222644, 1999. a
Coles, S. G.: An Introduction to Statistical Modeling of Extreme Values,
Springer-Verlag, London, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b
Cornes, R., Schrier, G., Van den Besselaar, E., and Jones, P.: An ensemble
version of the E-OBS temperature and precipitation data sets, J.
Geophys. Res.-Atmos., 123, 9391–9409,
https://doi.org/10.1029/2017JD028200, 2018. a, b
Davison, A. C. and Smith, R. L.: Models for exceedances over high thresholds,
J. Roy. Stat. Soc. Ser. B, 52,
393–442, https://doi.org/10.1111/j.2517-6161.1990.tb01796.x, 1990. a
de Bono, A., Giuliani, G., Kluser, S., and Peduzzi, P.: Impacts of summer 2003
heat wave in Europe, UNEP/DEWA/GRID Eur. Environ. Alert Bull., 2, 1–4,
https://www.unisdr.org/files/1145_ewheatwave.en.pdf (last access: 16 May 2023), 2004. a
Doblas-Reyes, F., Sörensson, A., Almazroui, M., Dosio, A., Gutowski, W.,
Haarsma, R., Hamdi, R., Hewitson, B., Kwon, W.-T., Lamptey, B., Maraun, D.,
Stephenson, T., Takayabu, I., Terray, L., Turner, A., and Zuo, Z.: Linking
Global to Regional Climate Change, in: Climate Change 2021: The
Physical Science Basis, Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S.,
Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E.,
Matthews, J. B. R., Maycock, T. K., Waterfield, T. K., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA,
https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10/ (last access: 16 May 2023), 2021. a
Donat, M. and Alexander, L.: The shifting probability distribution of global
daytime and night‐time temperatures, Geophys. Res. Lett., 39, L14707,
https://doi.org/10.1029/2012GL052459, 2012. a
Dunn, R. J. H., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M., Herold,
N., Lippmann, T., Allan, R., Aguilar, E., Barry, A. A., Brunet, M., Caesar,
J., Chagnaud, G., Cheng, V., Cinco, T., Durre, I., de Guzman, R., Htay,
T. M., Wan Ibadullah, W. M., Bin Ibrahim, M. K. I., Khoshkam, M., Kruger, A.,
Kubota, H., Leng, T. W., Lim, G., Li-Sha, L., Marengo, J., Mbatha, S.,
McGree, S., Menne, M., de los Milagros Skansi, M., Ngwenya, S., Nkrumah, F.,
Oonariya, C., Pabon-Caicedo, J. D., Panthou, G., Pham, C., Rahimzadeh, F.,
Ramos, A., Salgado, E., Salinger, J., Sané, Y., Sopaheluwakan, A.,
Srivastava, A., Sun, Y., Timbal, B., Trachow, N., Trewin, B., van der
Schrier, G., Vazquez-Aguirre, J., Vasquez, R., Villarroel, C., Vincent, L.,
Vischel, T., Vose, R., and Bin Hj Yussof, M. N.: Development of an Updated
Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes:
HadEX3, J. Geophys. Res.-Atmos., 125, e2019JD032263,
doi10.1029/2019JD032263, 2020. a
European Climate Assessment & Dataset project with Horizon 2020 EUSTACE project:
E-OBS v19.0HOM gridded dataset, https://www.ecad.eu/download/ensembles/downloadversion19.0eHOM.php, last access: 16 May 2023. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Farcomeni, A.: A review of modern multiple hypothesis testing, with particular
attention to the false discovery proportion, Stat. Meth. Med.
Res., 17, 347–388, https://doi.org/10.1177/0962280206079046, 2008. a
Fischer, E. and Schär, C.: Consistent geographical patterns of changes in
high-impact European heatwaves, Nat. Geosci., 3, 398–403,
https://doi.org/10.1038/ngeo866, 2010. a
Friederichs, P. and Thorarinsdottir, T. L.: Forecast verification for extreme
value distributions with an application to probabilistic peak wind
prediction, Environmetrics, 23, 579–594, https://doi.org/10.1002/env.2176, 2012. a
Gilleland, E. and Katz, R. W.: extRemes 2.0: An Extreme Value Analysis
Package in R, J. Stat. Softw., 72, 1–39,
https://doi.org/10.18637/jss.v072.i08, 2016. a
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and
estimation, J. Am. Stat. Assoc., 102, 359–378,
https://doi.org/10.1198/016214506000001437, 2007. a, b
Gneiting, T. and Ranjan, R.: Comparing density forecasts using threshold-and
quantile-weighted scoring rules, J. Bus. Econ. Stat.,
29, 411–422, https://doi.org/10.1198/jbes.2010.08110, 2011. a
Hastie, T. and Tibshirani, R.: Varying-coefficient models, J. Roy.
Stat. Soc. Ser. B, 55, 757–779,
https://doi.org/10.1111/j.2517-6161.1993.tb01939.x, 1993. a
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning: Data Mining, Inference and Prediction, Springer Series in
Statistics, Springer, New York, 2nd Edn., https://doi.org/10.1007/978-0-387-84858-7,
2009. a
Haug, O., Thorarinsdottir, T. L., Sørbye, S. H., and Franzke, C. L. E.: Spatial trend analysis of gridded temperature data at varying spatial scales, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, 2020. a, b
Heffernan, J. E. and Stephenson, A.: ismev: An Introduction to Statistical
Modeling of Extreme Values,
https://cran.r-project.org/web/packages/ismev/ismev.pdf (last access: 16 May 2023),
2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, doi10.1002/qj.3803, 2020. a
Hoegh-Guldberg, O., Jacob, D., Taylor, M., Bindi, M., Brown, S., Camilloni, I.,
Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., Guiot, J., Hijioka, Y.,
Mehrotra, S., Payne, A., Seneviratne, S., Thomas, A., Warren, R., and Zhou,
G.: Impacts of 1.5°C of Global Warming on Natural and Human
Systems, in: Global Warming of 1.5°C. An IPCC Special Report on
the impacts of global warming of 1.5°C above pre-industrial levels and
related global greenhouse gas emission pathways, in the context of
strengthening the global response to the threat of climate change,
sustainable development, and efforts to eradicate poverty, edited by: Masson-Delmotte,
V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani,
A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R.,
Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and
Waterfield, T., World Meteorological Organization, Geneva,
Switzerland, https://www.ipcc.ch/sr15/chapter/chapter-3/ (last access: 16 May 2023),
2018. a
Hofstra, N., Haylock, M., New, M., and Jones, P.: Testing E-OBS European
high-resolution gridded data set of daily precipitation and surface
temperature, J. Geophys. Res.-Atmos., 114, D21101,
https://doi.org/10.1029/2009JD011799, 2009. a, b
Hofstra, N., New, M., and McSweeney, C.: The influence of interpolation and
station network density on the distributions and trends of climate variables
in gridded daily data, Clim. Dynam., 35, 841–858,
https://doi.org/10.1007/s00382-009-0698-1, 2012. a
Hosking, J. R. M.: Maximum-likelihood estimation of the parameters of the
generalized extreme-value distribution, J. Roy. Stat.
Soc. Ser. C, 34, 301–310,
https://doi.org/10.1080/00949658308810625, 1985. a
Hosking, J. R. M.: L-Moments: Analysis and estimation of distributions using
linear combinations of order statistics, J. Roy. Stat.
Soc. Ser. B, 52, 105–124,
https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990. a
Hosking, J. R. M. and Wallis, J. R.: Regional Frequency Analysis: An Approach
Based on L-Moments, Cambridge University Press, New York,
https://doi.org/10.1017/CBO9780511529443, 2005. a
Hosking, J. R. M., 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, https://doi.org/10.1080/00401706.1985.10488049,
1985. a
IPCC: Climate Change 2014: Synthesis Report, Contribution of Working
Groups I, II and III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Core Writing Team,
edited by: Pachauri, R. K. and Meyer, L. A., IPCC, Geneva, Switzerland, 151 pp.,
https://www.ipcc.ch/report/ar5/syr/ (last access: 16 May 2023), 2014. a
IPCC: Atlas, in: Climate Change 2021: The Physical Science Basis.
Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P.,
Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb,
L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K.,
Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA,
https://www.ipcc.ch/report/ar6/wg1/chapter/atlas/ (last access: 16 May 2023),
2021a. a
IPCC: Summary for Policymakers, in: Climate Change 2021: The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V.,
Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y.,
Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T. K., Yelekçi, O., Yu, R., and Zhou, B., Cambridge
University Press,
https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf (last access: 16 May 2023),
2021b. a, b
Jones, P. and Hegerl, G.: Comparisons of two methods of removing
anthropogenically related variability from the near-surface observational
temperature field, J. Geophys. Res., 103, 13777–13786,
https://doi.org/10.1029/98JD01144, 1998. a
Jordan, A., Krüger, F., and Lerch, S.: Evaluating Probabilistic Forecasts
with scoringRules, J. Stat. Softw., 90, 1–37,
https://doi.org/10.18637/jss.v090.i12, 2019. a
Katz, R., Parlange, M., and Naveau, P.: Statistics of extremes in hydrology,
Adv. Water Resour., 25, 1287–1304,
https://doi.org/10.1016/S0309-1708(02)00056-8, 2002. a
Keeling, C. D., Bacastow, R. B., Bainbridge, A. E., Ekdahl Jr., C. A.,
Guenther, P. R., Waterman, L. S., and Chin, J. F. S.: Atmospheric carbon
dioxide variations at Mauna Loa Observatory, Hawaii, Tellus, 28,
538–551, https://doi.org/10.3402/tellusa.v28i6.11322, 1976. a
Kharin, V. V. and Zwiers, F. W.: Estimating extremes in transient climate
change simulations, J. Climate, 18, 1156–1173,
https://doi.org/10.1175/JCLI3320.1, 2005. a
Kiktev, D., Sexton, D. M. H., Alexander, L., and Folland, C. K.: Comparison of
modeled and observed trends in indices of daily climate extremes, J.
Climate, 16, 3560–3571,
doi10.1175/1520-0442(2003)016<3560:COMAOT>2.0.CO;2, 2003. a
Kim, Y.-H., Min, S.-K., Zhang, X., Sillmann, J., and Sandstad, M.: Evaluation
of the CMIP6 multi-model ensemble for climate extreme indices, Weather and
Climate Extremes, 29, 100269,
doi10.1016/j.wace.2020.100269, 2020. a
Kotlarski, S., Szabó, P., Herrera García, S., Räty, O., Keuler, K., Soares,
P., Cardoso, R., Bosshard, T., Page, C., Boberg, F., Gutiérrez, J., Isotta,
F., Jaczewski, A., Kreienkamp, F., Liniger, M., Lussana, C., and
Pianko-Kluczynska, K.: Observational uncertainty and regional climate model
evaluation: A pan-European perspective, Int. J. Climatol.,
39, 3730–3749, https://doi.org/10.1002/joc.5249, 2017. a
Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267–1277, https://doi.org/10.5194/hess-11-1267-2007, 2007. a
Martins, E. and Steidinger, J.: Generalized maximum‐likelihood generalized
extreme‐value quantile estimators for hydrologic data, Water Resources
Research, 36, 737–744, https://doi.org/10.1029/1999WR900330, 2000. a
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Morak, S., Hegerl, G. C., and Christidis, N.: Detectable changes in the
frequency of temperature extremes, J. Climate, 26, 1561–1574,
https://doi.org/10.1175/JCLI-D-11-00678.1, 2013. a
New, M. G., Lister, D. H., Hulme, M., and Makin, I. W.: A high-resolution data
set of surface climate over global land areas, Clim. Res., 21, 1–25,
2002. a
Northrop, P. and Jonathan, P.: Threshold modelling of spatially-dependent
non-stationary extremes with application to hurricane-induced wave heights,
J. Environ., 22, 799–809, https://doi.org/10.1002/env.1106, 2011. a
Resnick, S. I.: Heavy-Tail Phenomena: Probabilistic and Statistical Modeling,
Springer Series in Operations Research and Financial Engineering, Springer,
New York, https://doi.org/10.1007/978-0-387-45024-7, 2007. a
Ribatet, M., Cooley, D., and Davison, A.: Bayesian inference from composite
likelihoods, with an application to spatial extremes, Stat. Sinica, 22,
813–845, https://doi.org/10.5705/ss.2009.248, 2012. a, b, c, d
Robine, J.-M., Cheung, S. L. K., Le Roy, S., Van Oyen, H., Griffiths, C.,
Michel, J.-P., and Herrmann, F. R.: Death toll exceeded 70,000 in Europe
during the summer of 2003, C. R. Biol., 331, 171–178,
https://doi.org/10.1016/j.crvi.2007.12.001, 2008. a
Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020. a
Rue, H. and Held, L.: Gaussian Markov Random Fields: Theory and Applications,
vol. 104 of Monographs on Statistics and Applied Probability, Chapman
& Hall/CRC, New York, https://doi.org/10.1201/9780203492024, 2005. a, b, c
Rue, H., Martino, S., and Chopin, N.: Approximate Bayesian inference for
latent Gaussian models by using integrated nested Laplace approximations,
J. Roy. Stat. Soc. Ser. B, 71,
319–392, https://doi.org/10.1111/j.1467-9868.2008.00700.x, 2009. a
Schrier, G., Van den Besselaar, E., Tank, A., and Verver, G.: Monitoring
European average temperature based on the E-OBS gridded data set, J. Geophys. Res.-Atmos., 118, 5120–5135,
https://doi.org/10.1002/jgrd.50444, 2013. a
Schär, C., Vidale, P., Lüthi, D., Frei, C., Häberli, C., Liniger, M., and
Appenzeller, C.: The role of increasing temperature variability in European
summer heatwaves, Nature, 427, 332–336, https://doi.org/10.1038/nature02300, 2004. a
Smith, R. L.: Maximum likelihood estimation in a class of non-regular cases,
Biometrika, 72, 67–90, https://doi.org/10.1093/biomet/72.1.67, 1985. a
Squintu, A. A., van der Schrier, G., Brugnara, Y., and Klein Tank, A.:
Homogenization of daily temperature series in the European Climate
Assessment & Dataset, Int. J. Climatol., 39, 1243–1261,
https://doi.org/10.1002/joc.5874, 2019. a
Stips, A., Macías, D., Eayrs, C., Garcia-Gorriz, E., and Liang, X. S.: On the
causal structure between CO2 and global temperature, Sci. Rep., 6,
21691, https://doi.org/10.1038/srep21691, 2016. a
Stone, M.: Cross-validatory choice and assessment of statistical predictions,
J. Roy. Stat. Soc. Ser. B, 36,
111–133, https://doi.org/10.1111/j.2517-6161.1974.tb00994.x, 1974. a
Stott, P. A., Stone, D., and Allen, M.: Human contribution to the European
heatwave of 2003, Nature, 432, 610–614, https://doi.org/10.1038/nature03089, 2004.
a
Thorarinsdottir, T. L., Sillmann, J., Haugen, M., Gissibl, N., and Sandstad,
M.: Evaluation of CMIP5 and CMIP6 simulations of historical surface air
temperature extremes using proper evaluation methods, Environ. Res.
Lett., 15, 124041, https://doi.org/10.1088/1748-9326/abc778, 2020. a
Titley, D., Hegerl, G., Jacobs, K., Mote, P., Paciorek, C., Shepherd, J.,
Shepherd, T., Sobel, A., Walsh, J., and Zwiers, F.: Attribution of Extreme
Weather Events in the Context of Climate Change, National Academies Press,
https://doi.org/10.17226/21852, 2016. a
Weaver, S., Kumar, A., and Chen, M.: Recent increases in extreme temperature
occurrence over land, Geophys. Res. Lett., 41, 4669–4675,
https://doi.org/10.1002/2014GL060300, 2014. a
Wood, S.: Generalized Additive Models: An Introduction with R, CRC Press,
United States, 2nd Edn., https://doi.org/10.1201/9781315370279, 2017. a
Youngman, B. D.: Generalized additive models for exceedances of high thresholds
with an application to return level estimation for U.S. wind gusts, J. Am. Stat. Assoc., 114, 1865–1879,
https://doi.org/10.1080/01621459.2018.1529596, 2019. a
Zwiers, F. W., Zhang, X., and Feng, Y.: Anthropogenic influence on long return
period daily temperature extremes at regional scales, J. Climate,
24, 881–892, https://doi.org/10.1175/2010JCLI3908.1, 2011. a, b, c, d
Short summary
In this paper we consider the problem of detecting changes in the distribution of the annual maximum temperature, during the years 1950–2018, across Europe.
We find that, on average, the temperature that would be expected to be exceeded
approximately once every 100 years in the 1950 climate is expected to be exceeded once every 6 years in the 2018 climate. This is of concern due to the devastating effects on humans and natural systems that are caused by extreme temperatures.
In this paper we consider the problem of detecting changes in the distribution of the annual...