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.
24 May 2023
| 24 May 2023
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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Lauren R. Marshall, Anja Schmidt, Andrew P. Schurer, Nathan Luke Abraham, Lucie J. Lücke, Rob Wilson, Kevin Anchukaitis, Gabriele Hegerl, Ben Johnson, Bette L. Otto-Bliesner, Esther C. Brady, Myriam Khodri, and Kohei Yoshida
EGUsphere, https://doi.org/10.5194/egusphere-2024-1322, https://doi.org/10.5194/egusphere-2024-1322, 2024
This preprint is open for discussion and under review for Climate of the Past (CP).
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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.
Nathan P. Gillett, Hideo Shiogama, Bernd Funke, Gabriele Hegerl, Reto Knutti, Katja Matthes, Benjamin D. Santer, Daithi Stone, and Claudia Tebaldi
Geosci. Model Dev., 9, 3685–3697, https://doi.org/10.5194/gmd-9-3685-2016, https://doi.org/10.5194/gmd-9-3685-2016, 2016
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Detection and attribution of climate change is the process of determining the causes of observed climate changes, which has underpinned key conclusions on the role of human influence on climate in the reports of the Intergovernmental Panel on Climate Change (IPCC). This paper describes a coordinated set of climate model experiments that will form part of the Sixth Coupled Model Intercomparison Project and will support improved attribution of climate change in the next IPCC report.
Davide Zanchettin, Myriam Khodri, Claudia Timmreck, Matthew Toohey, Anja Schmidt, Edwin P. Gerber, Gabriele Hegerl, Alan Robock, Francesco S. R. Pausata, William T. Ball, Susanne E. Bauer, Slimane Bekki, Sandip S. Dhomse, Allegra N. LeGrande, Graham W. Mann, Lauren Marshall, Michael Mills, Marion Marchand, Ulrike Niemeier, Virginie Poulain, Eugene Rozanov, Angelo Rubino, Andrea Stenke, Kostas Tsigaridis, and Fiona Tummon
Geosci. Model Dev., 9, 2701–2719, https://doi.org/10.5194/gmd-9-2701-2016, https://doi.org/10.5194/gmd-9-2701-2016, 2016
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Simulating volcanically-forced climate variability is a challenging task for climate models. The Model Intercomparison Project on the climatic response to volcanic forcing (VolMIP) – an endorsed contribution to CMIP6 – defines a protocol for idealized volcanic-perturbation experiments to improve comparability of results across different climate models. This paper illustrates the design of VolMIP's experiments and describes the aerosol forcing input datasets to be used.
T. Russon, A. W. Tudhope, G. C. Hegerl, M. Collins, and J. Tindall
Clim. Past, 9, 1543–1557, https://doi.org/10.5194/cp-9-1543-2013, https://doi.org/10.5194/cp-9-1543-2013, 2013
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Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
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A multi-method framework for global real-time climate attribution
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Comparing climate time series – Part 3: Discriminant analysis
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
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A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
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The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
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Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
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Future climate emulations using quantile regressions on large ensembles
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Svenja Szemkus and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 29–49, https://doi.org/10.5194/ascmo-10-29-2024, https://doi.org/10.5194/ascmo-10-29-2024, 2024
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This paper uses the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019), which we extend to the description of common extremes in two variables. We develop an extreme pattern index (EPI), a pattern-based aggregation to describe spatially extended weather extremes. Our results show that the EPI is suitable for describing heat waves. We extend the EPI to describe extremes in two variables and obtain an index to describe compound precipitation deficits and heat waves.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 29–44, https://doi.org/10.5194/ascmo-9-29-2023, https://doi.org/10.5194/ascmo-9-29-2023, 2023
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Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
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Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
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The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Daniel M. Gilford, Andrew Pershing, Benjamin H. Strauss, Karsten Haustein, and Friederike E. L. Otto
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 135–154, https://doi.org/10.5194/ascmo-8-135-2022, https://doi.org/10.5194/ascmo-8-135-2022, 2022
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We developed a framework to produce global real-time estimates of how human-caused climate change affects the likelihood of daily weather events. A multi-method approach provides ensemble attribution estimates accompanied by confidence intervals, creating new opportunities for climate change communication. Methodological efficiency permits daily analysis using forecasts or observations. Applications with daily maximum temperature highlight the framework's capacity on daily and global scales.
Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
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We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
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A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
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Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
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The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
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We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
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Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
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Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
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We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
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Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
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Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
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Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
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This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
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A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
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We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
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Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
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Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
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In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
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...
