Articles | Volume 21, issue 6
https://doi.org/10.5194/nhess-21-1867-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1867-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards a compound-event-oriented climate model evaluation: a decomposition of the underlying biases in multivariate fire and heat stress hazards
Roberto Villalobos-Herrera
CORRESPONDING AUTHOR
School of Engineering, Newcastle University, Newcastle upon Tyne, NE2 1HA, UK
Escuela de Ingeniería Civil, Universidad de Costa Rica, Montes de Oca, San José 1150-2060, Costa Rica
Emanuele Bevacqua
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Andreia F. S. Ribeiro
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, Zurich 8092, Switzerland
Graeme Auld
School of Mathematics, The University of Edinburgh, Edinburgh, UK
Laura Crocetti
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
Bilyana Mircheva
Department of Meteorology and Geophysics, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria
Minh Ha
Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université, Paris and Guyancourt, France
Jakob Zscheischler
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Climate and Environmental Physics, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Carlo De Michele
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
Related authors
No articles found.
Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2025-3006, https://doi.org/10.5194/egusphere-2025-3006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.
Lou Brett, Christopher J. White, Daniela I. V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
Nat. Hazards Earth Syst. Sci., 25, 2591–2611, https://doi.org/10.5194/nhess-25-2591-2025, https://doi.org/10.5194/nhess-25-2591-2025, 2025
Short summary
Short summary
Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events, and compounding flooding. The review also highlights opportunities for research in the coming years.
Bastien François, Khalil Teber, Lou Brett, Richard Leeding, Luis Gimeno-Sotelo, Daniela I. V. Domeisen, Laura Suarez-Gutierrez, and Emanuele Bevacqua
Earth Syst. Dynam., 16, 1029–1051, https://doi.org/10.5194/esd-16-1029-2025, https://doi.org/10.5194/esd-16-1029-2025, 2025
Short summary
Short summary
Spatially compounding wind and precipitation (CWP) extremes can lead to severe impacts on society. We find that concurrent climate variability modes favor the occurrence of such wintertime spatially compounding events in the Northern Hemisphere and can even amplify the number of regions and population exposed. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management of such spatially compounding events.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025, https://doi.org/10.5194/amt-18-57-2025, 2025
Short summary
Short summary
This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
Daniel Klotz, Peter Miersch, Thiago V. M. do Nascimento, Fabrizio Fenicia, Martin Gauch, and Jakob Zscheischler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-450, https://doi.org/10.5194/essd-2024-450, 2025
Preprint under review for ESSD
Short summary
Short summary
Data availability is central to hydrological science. It is the basis for advancing our understanding of hydrological processes, building prediction models, and anticipatory water management. We present a data-driven daily runoff reconstruction product for natural streamflow. We name it EARLS: European aggregated reconstruction for large-sample studies. The reconstructions represent daily simulations of natural streamflow across Europe and cover the period from 1953 to 2020.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Daniel Klotz, Martin Gauch, Frederik Kratzert, Grey Nearing, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3665–3673, https://doi.org/10.5194/hess-28-3665-2024, https://doi.org/10.5194/hess-28-3665-2024, 2024
Short summary
Short summary
The evaluation of model performance is essential for hydrological modeling. Using performance criteria requires a deep understanding of their properties. We focus on a counterintuitive aspect of the Nash–Sutcliffe efficiency (NSE) and show that if we divide the data into multiple parts, the overall performance can be higher than all the evaluations of the subsets. Although this follows from the definition of the NSE, the resulting behavior can have unintended consequences in practice.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Short summary
Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary
Short summary
Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Derrick Muheki, Axel A. J. Deijns, Emanuele Bevacqua, Gabriele Messori, Jakob Zscheischler, and Wim Thiery
Earth Syst. Dynam., 15, 429–466, https://doi.org/10.5194/esd-15-429-2024, https://doi.org/10.5194/esd-15-429-2024, 2024
Short summary
Short summary
Climate change affects the interaction, dependence, and joint occurrence of climate extremes. Here we investigate the joint occurrence of pairs of river floods, droughts, heatwaves, crop failures, wildfires, and tropical cyclones in East Africa under past and future climate conditions. Our results show that, across all future warming scenarios, the frequency and spatial extent of these co-occurring extremes will increase in this region, particularly in areas close to the Nile and Congo rivers.
Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
Short summary
Short summary
Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
Short summary
Short summary
We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
F. Ioli, E. Bruno, D. Calzolari, M. Galbiati, A. Mannocchi, P. Manzoni, M. Martini, A. Bianchi, A. Cina, C. De Michele, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, 2023
Colin Manning, Martin Widmann, Douglas Maraun, Anne F. Van Loon, and Emanuele Bevacqua
Weather Clim. Dynam., 4, 309–329, https://doi.org/10.5194/wcd-4-309-2023, https://doi.org/10.5194/wcd-4-309-2023, 2023
Short summary
Short summary
Climate models differ in their representation of dry spells and high temperatures, linked to errors in the simulation of persistent large-scale anticyclones. Models that simulate more persistent anticyclones simulate longer and hotter dry spells, and vice versa. This information is important to consider when assessing the likelihood of such events in current and future climate simulations so that we can assess the plausibility of their future projections.
Shijie Jiang, Emanuele Bevacqua, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 26, 6339–6359, https://doi.org/10.5194/hess-26-6339-2022, https://doi.org/10.5194/hess-26-6339-2022, 2022
Short summary
Short summary
Using a novel explainable machine learning approach, we investigated the contributions of precipitation, temperature, and day length to different peak discharges, thereby uncovering three primary flooding mechanisms widespread in European catchments. The results indicate that flooding mechanisms have changed in numerous catchments over the past 70 years. The study highlights the potential of artificial intelligence in revealing complex changes in extreme events related to climate change.
Natacha Le Grix, Jakob Zscheischler, Keith B. Rodgers, Ryohei Yamaguchi, and Thomas L. Frölicher
Biogeosciences, 19, 5807–5835, https://doi.org/10.5194/bg-19-5807-2022, https://doi.org/10.5194/bg-19-5807-2022, 2022
Short summary
Short summary
Compound events threaten marine ecosystems. Here, we investigate the potentially harmful combination of marine heatwaves with low phytoplankton productivity. Using satellite-based observations, we show that these compound events are frequent in the low latitudes. We then investigate the drivers of these compound events using Earth system models. The models share similar drivers in the low latitudes but disagree in the high latitudes due to divergent factors limiting phytoplankton production.
Matthias Aichinger-Rosenberger, Elmar Brockmann, Laura Crocetti, Benedikt Soja, and Gregor Moeller
Atmos. Meas. Tech., 15, 5821–5839, https://doi.org/10.5194/amt-15-5821-2022, https://doi.org/10.5194/amt-15-5821-2022, 2022
Short summary
Short summary
This study develops an innovative approach for the detection and prediction of foehn winds. The approach uses products generated from GNSS (Global Navigation Satellite Systems) in combination with machine learning-based classification algorithms to detect and predict foehn winds at Altdorf, Switzerland. Results are encouraging and comparable to similar studies using meteorological data, which might qualify the method as an additional tool for short-term foehn forecasting in the future.
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
Short summary
Short summary
River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
Short summary
Short summary
Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler
Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, https://doi.org/10.5194/bg-19-1979-2022, 2022
Short summary
Short summary
Droughts and heatwaves are expected to occur more often in the future, but their effects on land vegetation and the carbon cycle are poorly understood. We use six climate scenarios with differing extreme occurrences and a vegetation model to analyse these effects. Tree coverage and associated plant productivity increase under a climate with no extremes. Frequent co-occurring droughts and heatwaves decrease plant productivity more than the combined effects of single droughts or heatwaves.
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, https://doi.org/10.5194/tc-16-1031-2022, 2022
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modelling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between April and September since higher temperatures protect snow from erosion. We also compared modelled and observed firn density, obtaining a satisfying agreement.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 1587–1601, https://doi.org/10.5194/hess-25-1587-2021, https://doi.org/10.5194/hess-25-1587-2021, 2021
Short summary
Short summary
We introduce a daily-scale index, termed the standardized compound drought and heat index (SCDHI), to measure the key features of compound dry-hot conditions. SCDHI can not only monitor the long-term compound dry-hot events, but can also capture such events at sub-monthly scale and reflect the related vegetation activity impacts. The index can provide a new tool to quantify sub-monthly characteristics of compound dry-hot events, which are vital for releasing early and timely warning.
Natacha Le Grix, Jakob Zscheischler, Charlotte Laufkötter, Cecile S. Rousseaux, and Thomas L. Frölicher
Biogeosciences, 18, 2119–2137, https://doi.org/10.5194/bg-18-2119-2021, https://doi.org/10.5194/bg-18-2119-2021, 2021
Short summary
Short summary
Marine ecosystems could suffer severe damage from the co-occurrence of a marine heat wave with extremely low chlorophyll concentration. Here, we provide a first assessment of compound marine heat wave and
low-chlorophyll events in the global ocean from 1998 to 2018. We reveal hotspots of these compound events in the equatorial Pacific and in the Arabian Sea and show that they mostly occur in summer at high latitudes and their frequency is modulated by large-scale modes of climate variability.
Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph A. Sauter, Elisabeth Tschumi, Karin van der Wiel, Tianyi Zhang, and Jakob Zscheischler
Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, https://doi.org/10.5194/esd-12-151-2021, 2021
Short summary
Short summary
We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
Short summary
Short summary
The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357, https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
Short summary
Short summary
Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modeling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between May and September, since higher temperatures protect snow from erosion. We also compared modeled and observed firn density obtaining a satisfying agreement.
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
Short summary
Short summary
Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
Andreia Filipa Silva Ribeiro, Ana Russo, Célia Marina Gouveia, Patrícia Páscoa, and Jakob Zscheischler
Biogeosciences, 17, 4815–4830, https://doi.org/10.5194/bg-17-4815-2020, https://doi.org/10.5194/bg-17-4815-2020, 2020
Short summary
Short summary
This study investigates the impacts of compound dry and hot extremes on crop yields, namely wheat and barley, over two regions in Spain dominated by rainfed agriculture. We provide estimates of the conditional probability of crop loss under compound dry and hot conditions, which could be an important tool for responsible authorities to mitigate the impacts magnified by the interactions between the different hazards.
Cited articles
ASCM – American College of Sports Medicine: Prevention of Thermal Injuries During Distance Running – Position stand, Med. Sci. Sport. Exerc., 16, ix–xiv, 1984.
Berrisford, P., Kållberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.:
Atmospheric conservation properties in ERA-Interim,
Q. J. Roy. Meteor. Soc.,
137, 1381–1399, https://doi.org/10.1002/qj.864, 2011.
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017.
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.:
Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change,
Science Advances,
5, 9, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019.
Bevacqua, E., Vousdoukas, M. I., Shepherd, T. G., and Vrac, M.: Brief communication: The role of using precipitation or river discharge data when assessing global coastal compound flooding, Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, 2020a.
Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., Mentaschi, L., and Feyen, L.: More meteorological events that drive compound coastal flooding are projected under climate change, Commun. Earth Environ., 1, 47, https://doi.org/10.1038/s43247-020-00044-z, 2020b.
Bevacqua, E., Zappa, G., and Shepherd, T. G.: Shorter cyclone clusters modulate changes in European wintertime precipitation extremes,
Environ. Res. Lett., 15, 124005, https://doi.org/10.1088/1748-9326/abbde7, 2020c.
Brando, P. M., Balch, J. K., Nepstad, D. C., Morton, D. C., Putz, F. E., Coe, M. T., Silvério, D., Macedo, M. N., Davidson, E. A., Nóbrega, C. C., Alencar, A., and Soares-Filho, B. S.:
Abrupt increases in Amazonian tree mortality due to drought-fire interactions,
P. Natl. Acad. Sci. USA,
111, 6347–6352, https://doi.org/10.1073/pnas.1305499111, 2014.
Dale, M. and Fortin, M.:
Spatial Autocorrelation and Statistical Tests: Some Solutions,
J. Agr. Biol. Envir. St.,
14, 188–206, 2009.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.:
The ERA-Interim reanalysis: configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Durante, F. and Sempi, C.: Principles of Copula Theory, 1st Edn., Chapman and Hall/CRC, https://doi.org/10.1201/b18674, 2015.
ECMWF: Public Datasets, available at: https://apps.ecmwf.int/datasets/, last access: June 2021.
FIALA, D., Havenith, G., Bröde, P., Kampmann, B., and Jendritzky, G.:
UTCI-Fiala multi-node model of human heat transfer and temperature regulation,
Int. J. Biometeorol.,
Special Issue, 1–13, 2011.
Fischer, E. M. and Knutti, R.:
Robust projections of combined humidity and temperature extremes,
Nat. Clim. Change,
3, 126–130, https://doi.org/10.1038/nclimate1682, 2013.
François, B., Vrac, M., Cannon, A. J., Robin, Y., and Allard, D.: Multivariate bias corrections of climate simulations: which benefits for which losses?, Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, 2020.
Hobæk Haff, I., Frigessi, A., and Maraun, D.:
How well do regional climate models simulate the spatial dependence of precipitation? An application of pair-copula constructions,
J. Geophys. Res.-Atmos.,
120, 2624–2646, https://doi.org/10.1002/2014JD022748, 2015.
Hofert, M., Kojadinovic, I., Maechler, M., and Yan, J.:
copula: Multivariate Dependence with Copulas, R package version 0.999-19.1,
available at: https://CRAN.R-project.org/package=copula (last access: 19 November 2020), 2018.
Hollander, M., Wolfe, D. A., and Chicken, E.: Nonparametric Statistical Methods, 3rd Edn., John Wiley & Sons, Hoboken, New Jersey, 2014.
Jafari, M. and Ansari-Pour, N.:
Why, When and How to Adjust Your P Values?,
Cell J.,
20, 604–607, https://doi.org/10.22074/cellj.2019.5992, 2019.
Jézéquel, A., Bevacqua, E., D'Andrea, F., Thao, S., Vautard, R., Vrac, M., and Yiou, P.:
Conditional and residual trends of singular hot days in Europe,
Environ. Res. Lett.,
15, 064018, https://doi.org/10.1088/1748-9326/ab76dd, 2020.
Kornhuber, K., Coumou, D., Vogel, E., Lesk, C., Donges, J. F., Lehmann, J., and Horton, R. M.:
Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions,
Nat. Clim. Change,
10, 48–53, 2020.
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., Mcinnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M.:
A compound event framework for understanding extreme impacts,
WIREs Clim. Change,
5, 113–128, https://doi.org/10.1002/wcc.252, 2014.
Manning, C., Widmann, M., Bevacqua, E., Van Loon, A. F., Maraun, D., and Vrac, M.:
Soil moisture drought in Europe: a compound event of precipitation and potential evapotranspiration on multiple timescales,
J. Hydrometeorol.,
19, 1255–1271, https://doi.org/10.1175/JHM-D-18-0017.1, 2018.
Manning, C., Widmann, M., Bevacqua, E., Van Loon, A. F., Maraun, D., and Vrac, M.:
Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013),
Environ. Res. Lett.,
14, 094006, https://doi.org/10.1088/1748-9326/ab23bf, 2019.
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review,
Current Climate Change Reports, 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016.
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.:
Towards process-informed bias correction of climate change simulations,
Nat. Clim. Change,
7, 764–773, https://doi.org/10.1038/nclimate3418, 2017.
McCutchan, M. H. and Main, W. A.: The relationship between mean monthly fire potential indices and monthly fire severity, in: Proceedings of the 10th Conference on Fire and Forest Meteorology, edited by: MacIver, D. C., Auld, H., and Whitewood, R., Forestry Canada, Ottawa, Ontario, Canada, 430–435, 1989.
Nelsen, R. B.: An Introduction to Copulas, in: Springer Series in Statistics, 2nd Edn., XIV, 272, Springer, New York, NY, https://doi.org/10.1007/0-387-28678-0, 2006.
Pfahl, S., O'Gorman, P., and Fischer, E.:
Understanding the regional pattern of projected future changes in extreme precipitation,
Nat. Clim. Change,
7, 423–427, https://doi.org/10.1038/nclimate3287, 2017.
Priestley, M. D., Pinto, J. G., Dacre, H. F., and Shaffrey, L. C.:
The role of cyclone clustering during the stormy winter of 2013/2014,
Weather,
72, 187–192, 2017.
Raymond, C., Matthews, T., and Horton, R. M.:
The emergence of heat and humidity too severe for human tolerance,
Science Advances,
6, eaaw1838, https://doi.org/10.1126/sciadv.aaw1838, 2020.
R Core Team: R: A language and environment for statistical computing,
R Foundation for Statistical Computing, Vienna, Austria,
available at: https://www.R-project.org/ (last access: 19 November 2020), 2019.
Remillard, B. and Plante, J.-F.:
TwoCop: Nonparametric test of equality between two copulas,
R package version 1.0,
available at: https://CRAN.R-project.org/package=TwoCop (last access: 19 November 2020), 2012.
Rémillard, B. and Scaillet, O.:
Testing for equality between two copulas,
J. Multivariate Anal.,
100, 377–386, https://doi.org/10.1016/j.jmva.2008.05.004, 2009.
Roads, J. P., Tripp, P., Juang, H., Wang, J., Chen, S., and Fujioka, F.: ECPC/NCEP March 2008 seasonal fire danger forecasts, in: Experimental Long-Lead Forecasts Bulletin, 17, National Centers for Environmental Prediction, Camp Springs, Maryland, 7 pp., 2008.
Russo, S., Sillmann, J., and Sterl, A.:
Humid heat waves at different warming levels,
Sci. Rep.,
7, 1–7. https://doi.org/10.1038/s41598-017-07536-7, 2017.
Salvadori, G. and De Michele, C.:
On the Use of Copulas in Hydrology: Theory and Practice,
J. Hydrol. Eng.,
12, 369–380, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(369), 2007.
Salvadori, G., De Michele, C., Kottegoda, N. T., and Rosso, R.:
Extreme in nature: An approach using copulas,
Springer, Dordrecht, 2007.
Schär, C.:
Climate extremes:
The worst heat waves to come,
Nat. Clim. Change,
6, 128–129, https://doi.org/10.1038/nclimate2864, 2016.
Schneider, G., Chicken, E., and Becvarik, R.:
NSM3: Functions and Datasets to Accompany Hollander, Wolfe, and Chicken – Nonparametric Statistical Methods, third edn.,
R package version 1.15,
available at: https://CRAN.R-project.org/package=NSM3 (last access: 19 November 2020), 2020.
Scholz, F. and Zhu, A.:
kSamples: K-Sample Rank Tests and their Combinations,
R package version 1.2-9,
available at: https://CRAN.R-project.org/package=kSamples (last access: 19 November 2020), 2019.
Sklar, A.: Fonctions de Répartition à n Dimensions et Leurs Marges,
Publications de l'Institut Statistique de l'Université de Paris, Paris, 8, 229–231, 1959.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.:
An Overview of CMIP5 and the experiment design,
B. Am. Meteorol. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Van Wagner, C. E.:
Development and structure of the canadian forest fire weather index system, Technical Report 35,
Can. Forestry Serv., Ottawa, Ontario, 48, 1987.
Vezzoli, R., Salvadori, G., and De Michele, C.:
A distributional multivariate approach for assessing performance of climate-hydrology models,
Sci. Rep.,
7, 1–15, https://doi.org/10.1038/s41598-017-12343-1, 2017.
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018.
WCRP: Coupled Model Intercomparison Project 5 (CMIP5), available at: https://esgf-node.llnl.gov/projects/cmip5, last access: June 2021.
Yue, S., Pilon, P., Phinney, B., and Cavadias, G.:
The influence of autocorrelation on the ability to detect trend in hydrological series,
Hydrol. Process.,
16, 1807–1829, https://doi.org/10.1002/hyp.1095, 2002.
Zscheischler, J. and Fischer, E. M.:
The record-breaking compound hot and dry 2018 growing season in Germany,
Weather and Climate Extremes,
29, 100270, https://doi.org/10.1016/j.wace.2020.100270, 2020.
Zscheischler, J. and Seneviratne, S. I.:
Dependence of drivers affects risks associated with compound events,
Science Advances,
3, 1–11, https://doi.org/10.1126/sciadv.1700263, 2017.
Zscheischler, J., Westra, S., Van Den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., Aghakouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.:
Future climate risk from compound events,
Nat. Clim. Change,
8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Zscheischler, J., Fischer, E. M., and Lange, S.: The effect of univariate bias adjustment on multivariate hazard estimates, Earth Syst. Dynam., 10, 31–43, https://doi.org/10.5194/esd-10-31-2019, 2019.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., R., C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N., Thiery, W., and Vignotto, E.:
A typology of compound weather and climate events,
Nature Reviews Earth & Environment,
1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020.
Zscheischler, J., Naveau, P., Martius, O., Engelke, S., and Raible, C. C.: Evaluating the dependence structure of compound precipitation and wind speed extremes, Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, 2021.
Short summary
Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Climate hazards may be caused by events which have multiple drivers. Here we present a method to...
Altmetrics
Final-revised paper
Preprint