Articles | Volume 6, issue 1
https://doi.org/10.5194/ascmo-6-61-2020
© Author(s) 2020. 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-6-61-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Jun 2020
| 08 Jun 2020
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Meagan Carney
CORRESPONDING AUTHOR
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
Holger Kantz
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
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Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Imre M. Jánosi, Holger Kantz, Jason A. C. Gallas, and Miklós Vincze
Ocean Sci., 18, 1361–1375, https://doi.org/10.5194/os-18-1361-2022, https://doi.org/10.5194/os-18-1361-2022, 2022
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Surface flow fields of the global oceans are dominated by so-called mesoscale (50–300 km) eddies. They usually drift westward at a few kilometers per day, transporting mass, temperature, chlorophyll, and debris. There are several methods to identify and track eddies based on satellite measurements, some of them very computationally demanding. Here we extend a recently proposed simple procedure to the global scale, which gives quick coarse-grained statistics on mesoscale vortex properties.
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022, https://doi.org/10.5194/esd-13-1157-2022, 2022
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The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on both tropical and extratropical weather extremes. In this study, we use machine learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 15, 4147–4161, https://doi.org/10.5194/gmd-15-4147-2022, https://doi.org/10.5194/gmd-15-4147-2022, 2022
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A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz’s system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
Imre M. Jánosi, Amin Padash, Jason A. C. Gallas, and Holger Kantz
Ocean Sci., 18, 307–320, https://doi.org/10.5194/os-18-307-2022, https://doi.org/10.5194/os-18-307-2022, 2022
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Spectacular climatic phenomena such as El Nino—La Nina oscillations are connected with large-scale rearrangements of oceanic surface flow patterns. In order to get a better insight into the dynamics of such changes, we performed numerical experiments on the advection of 6600 water parcels in the focal area. Surface flow fields were taken from the AVISO data bank. A simple stochastic model (fractional Brownian motion) with only two parameters nicely reproduced the statistics of advection.
R. C. Batac and H. Kantz
Nonlin. Processes Geophys., 21, 735–744, https://doi.org/10.5194/npg-21-735-2014, https://doi.org/10.5194/npg-21-735-2014, 2014
Related subject area
Climate research
Environmental sensitivity of the Caribbean economic growth rate
Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency
Changes in the distribution of annual maximum temperatures in Europe
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Comparing climate time series – Part 4: Annual cycles
Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
A multi-method framework for global real-time climate attribution
Analysis of the evolution of parametric drivers of high-end sea-level hazards
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
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
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
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Mark R. Jury
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 95–104, https://doi.org/10.5194/ascmo-10-95-2024, https://doi.org/10.5194/ascmo-10-95-2024, 2024
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A unique link is found between the Caribbean GDP growth rate and the tropical climate system. Although the Pacific El Niño–Southern Oscillation governs some aspects of this link, the Walker circulation and associated humidity over the equatorial Atlantic emerge as leading predictors of economic prosperity in the central Antilles islands.
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.
Graeme Auld, Gabriele C. Hegerl, and Ioannis Papastathopoulos
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 45–66, https://doi.org/10.5194/ascmo-9-45-2023, https://doi.org/10.5194/ascmo-9-45-2023, 2023
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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.
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.
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
Azencott, R., Muravina, V., Hekmati, R., Zhang, W., and Paldino, M.: Automatic clustering in large sets of time series, Contributions to Partial Differential Equations and Applications, Comput. Meth. Appl. Sci., 47 65–75, 2018. a
Bador, M., Naveau, P., Gilleland, E., Castellá, M., and Arivelo, T:. Spacial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles and E-OBS over Europe, Weather and Climate Extremes, 9, 17–24, https://doi.org/10.1016/j.wace.2015.05.003, 2015. a
Balvanera, P., Siddique, I., Dee, L., Paquette, A., Isbell, F., Gonzalez, A., Byrnes, J., O'Connor, M., Hungate, B., and Griffin, J.: Linking biodiversity and ecosystem services: current uncertainties and the necessary next steps, BioScience, 64, 49–57, 2013. a
Bartos, M., Chester, M., Johnson, N., Gorman, B., Eisenberg, D., Linkov, I., and Bates, M.: Impacts of rising air temperatures on electric transmission ampacity and peak electricity load in the United States Environ. Res. Lett., 11, 114008, https://doi.org/10.1088/1748-9326/11/11/114008, 2016. a
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., and Courchamp, F.: Impacts of climate change on the future of biodiversity, Ecol. Lett., 15 367–377, 2012. a
Brown, S. J., Caesar, J., and Ferro, C. A. T.: Global changes in extreme daily temperature since 1950, J. Geophys. Res., 113, D05115, https://doi.org/10.1029/2006JD008091, 2008. a
Burillo, D.: Effects of Climate Change in Electric Power Infrastructures, Power System Stability, IntechOpen, 5, https://doi.org/10.5772/intechopen.82146, 2019. a
Caroni, C. and Panagoulia, D.:
Non-stationary modelling of extreme temperatures in a mountainous area of Greece, REVSTAT, 14, 217–228, 2016. a
Chen, T., Singh, P., Bassler, K.: Network community detection using modularity density measures, J. Stat. Mech., 2018, 053406, https://doi.org/10.1088/1742-5468/aabfc8, 2018. a
Cheng, L., AghaKouchak, A., Gilleland, E., and Katz, R. W.: Non-stationary Extreme Value Analysis in a Changing Climate, Climatic Change, 2, 353–369, https://doi.org/10.1007/s10584-014-1254-5, 2014. a
Christidis, N., Jones, G. S., and Stott, P. A.: Dramatically increasing chance of extremely hot summers since the 2003 European heatwave, Nat. Clim. Change, 5, 46–50, 2015. a
Cover, T. and Thomas, J.: Elements of Information Theory, John Wiley and Sons, 1991.
Dhillion, I., Guan, Y., and Kulis, B.: Kernel k-means, spectral clustering and normalized cuts, Conference on Knowledge Discovery and Data Mining (KDD) 22–25 August, Seattle, Washingtion, USA, 2004. a
Duch, J. and Arenas, A.: Community detection in complex networks using extremal optimization, Phys. Rev. E, 72, 027104, https://doi.org/10.1103/PhysRevE.72.027104, 2005. a, b
DWD Climate Data Center (CDC): Historical hourly station observations of precipitation for Germany, versionv006, available at: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/precipitation/historical/ (last access: 26 June 2019), 2018b. a
DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v007, available at: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/more_precip/historical/, last access: 5 August 2019. a
Erkwurzel, B., Boneham, J., Dalton, M. W., Heede, R., Mera, R. J., Allen, M. R., and Frumhoff, P. C.: The rise in global atmospheric CO2, surface temperature, and sea level from emissions traced to major carbon producers, Climactic Change, 144, 579–590, 2017. a
Finkel, J. and Katz, J. I.: Changing world extreme temperature statistics, Int. J. Climatol., 38, 2613–2617, 2018. a
Gao, M. and Zheng, H.: Nonstationary extreme value analysis of temperature extremes in China, Stoch. Env. Res. Risk A., 32, 1299–1315, 2018. a
Gasparrini, A., Guo, Y., Sera, F., Vicedo-Cabrera, A. M., Huber, V., Tong, S., Coelho, M. S. Z. S., Saldiva, P. H. N., Lavigne, E., Correa, P. M., Ortega, N. V., Kan, H., Osorio, S., Kyselý, J., Urban, A., Jaakkola, J. J. K., Ryti, N. R. I., Pascal, M., Goodman, P. G., Zeka, A., Michelozzi, P., Scortichini, M., Hashizume, M., Honda, Y., Hurtado-Diaz, M., Cruz, J. C., Seposo, X., Kim, H., Tobias, A., Iñiguez, C., Forsberg, B., Åström, D. O., Ragettli, M. S., Guo, Y. L., Wu, C., Zanobetti, A., Schwartz, J., Bell, M. L., Dang, T. N., Van, D. D., Heaviside, C., Vardoulakis, S., Hajat, S., Haines, A., and Armstrong, B.: Projections of temperature-related excess mortality under climate change scenarios, The Lancet Planetary Health, 1, E360–E367, 2017. a
Guimera, R. and Amaral, L. N.: Functional cartography of complex metabolic networks, Nature, 433, 895–900, 2005. a
Hamdi, Y., Duluc, C. M., and Rebour, V.: Temperature extremes: estimation of non-stationary return levels and associated uncertainties, Atmosphere, 9, 129, https://doi.org/10.3390/atmos9040129, 2018. a, b
Hansen, J., Ruedy, M., Sato, M., and Lo, K.: Global surface temperature change, Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345, 2010. a
Hansen, J., Sato, M., and Ruedy, R.: Perception of climate change, P. Natl. Acad. Sci. USA, 109, E2415–E2423, 2012. a
Hasan, H., Salam, N., and Adam, M.: Modelling extreme temperature in Malaysia using generalized extreme value distribution, Int. J. Math. Comput. Sc., 7, https://doi.org/10.5281/zenodo.1080273, 2013. a
Hosking, J. R. M.: L-moments: Analysis and Estimation of Distributions using Linear Combinations of Order Statistics, J. R. Stat. Soc. B., 52, 105–124, 1990. a
Hosking, J. R. M. and Wallis, J. R.: Regional Frequency Analysis: An approach based on L-moments, Cambridge University Press, 1997. a
Kaspar, F., Müller-Westermeier, G., Penda, E., Mächel, H., Zimmermann, K., Kaiser-Weiss, A., and Deutschländer, T.: Monitoring of climate change in Germany – data, products and services of Germany's National Climate Data Centre, Adv. Sci. Res., 10, 99–106, https://doi.org/10.5194/asr-10-99-2013, 2013. a
Kosanic, A., Kavcic, I., van Kleunen, M., and Harrison, S.: Climate change and climate change velocity analysis across Germany, Sci. Rep., 9, https://doi.org/10.1038/s41598-019-38720-6, 2019. a
Kyselý, J.: Temporal fluctuations in heat waves at Prague–Klementinum, the Czech Republic, from 1901–97, and their relationships to atmospheric circulation, Int. J. Climatol., 22, 33–50, 2002. a
Li, Z., Zhang, S., Wang, R.-S., Zhang, X.-S., and Chen, L.: Quantitative function for community detection, Phys. Rev. E, 77, 036109, https://doi.org/10.1103/PhysRevE.77.036109, 2008. a, b
Lucarini, V., Faranda, D., Freitas, A. C. M., Freitas, J., Tobias, K., Holland, M., Nicol, M., Todd, M., and Vaienti, S.: Extremes and Recurrence in Dynamical Systems, Wiley, 2016. a
Luxburg, U.: A Tutorial on Spectral Clustering, Stat. Comput., 17, 395–416, https://doi.org/10.1007/s11222-007-9033-z, 2007.
a, b, c, d
Pörtner, H.-O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N.: IPCC, 2019: Summary for Policymakers, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, in press, 2020. a
Rahmstorf, S. and Coumou, D.: Increase of extreme events in a warming world, P. Natl. Acad. Sci. USA, 108, 17905–17909, 2011. a
Roulston, M.: Estimating the errors on measured entropy and mutual information, Physica D, 125, 285–294, 1999. a
Schär, C., Vidale, P., Lüthi, D., Frei, C., Häberli, C., Liniger, M., and Appenzeller, C.: The role of increasing temperature variablility in European summer heatwaves, Nature, 427, 332–336, 2004. a
Schölkopf, B., Smola, A., and Müller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput., 10, 1299–1319, 1998. a
Sun, Y., Danila, B., Josíc, K., and Bassler, K.: Improved community structure detection using modified fine-tuning strategy, Europhysics Letters Association, 86, 2, https://doi.org/10.1209/0295-5075/86/28004, 2009. a
Thornton, H. E., Hoskins, B. J., and Scaife, A. A.: The role of temperature in the variability and extremes of electricity and gas demand in Great Britain, Environ. Res. Lett., 11, 11, https://doi.org/10.1088/1748-9326/11/11/114015, 2016. a
Tomczyk, A. and Sulikowska, A.: Heat waves in lowland Germany and their circulation-related conditions, Meterology and Atmospheric Physics, 130, 499–515, 2018. a
Treviño III, S., Sun, Y., Cooper, T., and Bassler, K.: Robust detection of hierarchical communities from Escherichia coli gene expression data, PLOS Comput. Biol., 8, E1002391, https://doi.org/10.1371/journal.pcbi.1002391, 2012. a
Wang, S., Gittens, A., and Mahoney, M.: Scalable Kernel K-Means Clustering with Nyström Approximation: Relative-Error Bounds, J. Mach. Learn. Res., 20, 1–49, 2019. a
Wergen, G. and Krug, J.: Record-breaking temperatures reveal a warming climate, Europhys. Lett., 92, 3, https://doi.org/10.1209/0295-5075/92/30008, 2010. a
Yuan, C. and Yang, H.: Research on K-Value Selection Method of K-Means Clustering Algorithm, Mutlidisciplinary Scientific Journal, 2, 226–235, https://doi.org/10.3390/j2020016, 2019. a
Zhang, W., Muravina, V., Azencott, R., Chu, Z., and Paldino, M.: Mutual information better quanitfies brain network architecture in children with epilepsy, Comput. Math. Methods M., 2018, 6142898, https://doi.org/10.1155/2018/6142898, 2018. a
Zscheischler, J., Mahecha, M., and Harmeling, S.: Climate Classifications: the Value of Unsupervised Clustering, Procedia Comput. Sci., 9, 897–906, 2012. a
Short summary
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.
Extremes in weather can have lasting effects on human health and resource consumption. Studying...
