Compound weather and climate events
Research Focus
Virtually all climate-related disasters are caused by compounding climate processes. Next to climate extremes, many large impacts are caused by an unfortunate combination of – often unknown – regular climate and weather phenomena. For instance, a devastating flood may be caused by the combination of high antecedent soil moisture and moderate rainfall. Current risk assessments fail to adequately represent such compound events. It is further unknown how well current climate and impact models simulate the relationships between compound climate features that cause extreme impacts. The development of new tools to study compound events is crucial given the pressing need to provide reliable climate risk projections for human societies.
Compound weather and climate events refer to a combination of drivers and/or hazards that contributes to societal and environmental risk. Based on a recently developed
compound event typology
, we aim to better understand, model and predict how compounding processes in space, time and between variables in the weather and climate domain lead to impacts in human and natural systems. Currently we focus on the impacts vegetation mortality, crop failure and floods. We use statistical analyses, process-based modelling and machine learning together with a variety of datasets including climate observations, remotely sensed data, climate model output, and impact model simulations.
Group Members
At UFZ
Dr. Jakob Zscheischler
(Group leader)
Dr. Emanuele Bevacqua
(Postdoc)
Dr. Shije Jiang
(Postdoc)
Beijing Fang (Postdoc)
Mohit Anand
(PhD student)
Lily-Belle Sweet
(PhD student)
Peter Miersch (PhD student)
Jonathan Wider (PhD student)
Sifang Feng (Guest PhD student)
Ongoing projects
eXtreme events: Artificial Intelligence for Detection and Attribution (XAIDA)
Climate is changing fast, with losses and damages experienced in every region and every sector. However the awareness of this fact remains limited. Of particular relevance is how climate change modifies and enhances extreme weather events. XAIDA, an EU-funded project, brings together the interdisciplinary expertise of a research consortium of 15 universities and research organizations that unites experts in machine learning, statistics and climate modeling.
Together we will design new methods and apply them to recent high-impact events to understand the role of climate change. Further, we will study if such events, or even more-intense ones, will occur in the future. We will collaborate with concerned stakeholders from different sectors to prepare risk assessment and adaptation strategies for extreme weather.
Identifying causal drivers of floods (CausalFlood)
Floods are among the most damaging natural hazards, causing thousands of deaths and millions of dollars in damages every year. Understanding which physical drivers cause floods is crucial for flood forecasting and managing flood risk under changing environmental conditions. CausalFlood will advance the development of a state-of-the-art causal inference method for the application to flood drivers. The method will be applied to well-calibrated high-resolution hydrological simulations across different catchments in Europe to identify key flood drivers.
Detecting compound climate features of extreme impacts (COMPOUNDX)
COMPOUNDX exploits multiple available data archives of climate and impact model output as well as observation-based datasets to (i) develop statistical tools to identify compound climate features of extreme impacts based on machine learning, (ii) apply the developed tools in the real-world observations, and (iii) evaluate process-based models in their ability to simulate compound features. The impacts considered in the project are crop failure, carbon cycle extremes, and floods.
Completed projects
Compound events in the ocean
Extreme events shape the structure of biological systems and affect the biogeochemical functions and services they provide for society in a fundamental manner. There is overwhelming evidence that ocean extreme events, such as marine heatwaves, will increase in frequency, duration and intensity under future global warming, pushing marine organisms, fisheries and ecosystems beyond the limits of their resilience. Of particular concern are compound events, which correspond to events with multiple concurrent or consecutive drivers (e.g. marine heatwaves co-occur with very low nutrient levels) resulting in extreme consequences for marine ecosystems. This PhD project analyses compound events in the ocean in observations and models and quantifies uncertainties in model projects.
Risk assessment of critical ecological thresholds in Amazonia and Cerrado
The hydrological cycle is changing across the tropics due to interactions between global climate change and deforestation. The impacts on natural systems can be large, persistent, and may have major consequences for the carbon cycle and atmospheric CO2 concentrations. Many of the largest impacts on forests, savannas, agricultural systems result from the occurrence of extreme weather events. Recent weather extreme events have already caused (a) fundamental changes in the structure of tropical forests, (b) widespread losses in agriculture output, and (c) catastrophic forest and savanna fires. In this project, we use statistical models to quantify the multiple climatic drivers of large impacts to tropical forests, savannas, agriculture, and disturbance regimes. We assess how climate change may alter the likelihood of compounding, catastrophic weather events to occur in the near future across Amazonia and Cerrado.
Machine learning for detecting compound climate drivers of extreme impacts
In this project, we use of a dynamic global vegetation model (LPX-Bern) to explore the ability of state-of-the-art machine learning techniques to identify climate and weather features that cause extreme reduction in vegetation productivity. LPX-Bern is forced with very long simulations from a weather generator to generate large amounts of impact data. We then use machine learning approaches such as Convolutional Neural Networks to identify climate conditions that are associated with extremely low vegetation productivity through classification.
New metrics for constraining multiple drivers of hazard and compound hazards
The impacts of climate extremes are often particularly severe when several hazards occur at the same time. Currently it is not clear whether current climate models can capture major changes in risk associated with climate-related hazards. Existing modelling approaches used to assess risk may therefore lead to serious mal-adaptation. This project develops new metrics to evaluate climate models with respect to multivariate relationships, extremes, and compound hazards and uses these metrics to constrain model ensembles with observations to improve projections of hazards and compound hazards. The project focuses on the compound hazards a) drought and heat, b) wind and precipitation extremes, as well as on the hazards c) human heat stress and d) fire risk.
Publications
Index:
You could use our publication index for further requests.
2023 (20)
- Bastos, A., Sippel, S., Frank, D., Mahecha, M.D., Zaehle, S., Zscheischler, J., Reichstein, M. (2023):
A joint framework for studying compound ecoclimatic events
Nat. Rev. Earth Environ. 4 (5), 333 - 350
full text (doi) - Bevacqua, E., Suarez-Gutierrez, L., Jézéquel, A., Lehner, F., Vrac, M., Yiou, P., Zscheischler, J. (2023):
Advancing research on compound weather and climate events via large ensemble model simulations
Nat. Commun. 14 , art. 2145
full text (doi) - Fan, X., Miao, C., Zscheischler, J., Slater, L., Wu, Y., Chai, Y., AghaKouchak, A. (2023):
Escalating hot-dry extremes amplify compound fire weather risk
Earth Future 11 (11), e2023EF003976
full text (doi) - Fu, J., Jiang, Y., Wang, X., Li, L., Ciais, P., Zscheischler, J., Wang, Y., Tang, Y., Müller, C., Webber, H., Yang, B., Wu, Y., Wang, Q., Cui, X., Huang, W., Liu, Y., Zhao, P., Piao, S., Zhou, F. (2023):
Extreme rainfall reduces one-twelfth of China’s rice yield over the last two decades
Nat. Food 4 (5), 416 - 426
full text (doi) - García-García, A., Cuesta-Valero, F.J., Miralles, D.G., Mahecha, M., Quaas, J., Reichstein, M., Zscheischler, J., Peng, J. (2023):
Soil heat extremes can outpace air temperature extremes
Nat. Clim. Chang. 13 (11), 1237 - 1241
full text (doi) - Gu, L., Gentine, P., Wang, H.-M., Slater, L.J., Sullivan, S.C., Chen, J., Zscheischler, J., Guo, S. (2023):
Large anomalies in future extreme precipitation sensitivity driven by atmospheric dynamics
Nat. Commun. 14 , art. 3197
full text (doi) - Huang, F., Zhang, Y., Zhang, Y., Shangguan, W., Li, Q., Li, L., Jiang, S. (2023):
Interpreting Conv-LSTM for spatio-temporal soil moisture prediction in China
Agriculture-Basel 13 (5), art. 971
full text (doi) - Le Grix, N., Cheung, W.L., Reygondeau, G., Zscheischler, J., Frölicher, T.L. (2023):
Extreme and compound ocean events are key drivers of projected low pelagic fish biomass
Glob. Change Biol. 29 (23), 6478 - 6492
full text (doi) - Li, J., Bevacqua, E., Wang, Z., Sitch, S., Arora, V., Arneth, A., Jain, A.K., Goll, D., Tian, H., Zscheischler, J. (2023):
Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss
Commun. Earth Environ. 4 , art. 197
full text (doi) - Li, X., Piao, S., Huntingford, C., Peñuelas, J., Yang, H., Xu, H., Chen, A., Friedlingstein, P., Keenan, T.F., Sitch, S., Wang, X., Zscheischler, J., Mahecha, M.D. (2023):
Global variations in critical drought thresholds that impact vegetation
Natl. Sci. Rev. 10 (5), nwad049
full text (doi) - Li, Y., Huang, S., Wang, H., Huang, Q., Li, P., Zheng, X., Wang, Z., Jiang, S., Leng, G., Li, J., Peng, J. (2023):
Warming and greening exacerbate the propagation risk from meteorological to soil moisture drought
J. Hydrol. 622, Part B , art. 129716
full text (doi) - Lo, Y.T.E., Mitchell, D.M., Buzan, J.R., Zscheischler, J., Schneider, R., Mistry, M.N., Kyselý, J., Lavigne, É., Pereira da Silva, S., Royé, D., Urban, A., Armstrong, B., Gasparrini, A., Vicedo-Cabrera, A.M. (2023):
Optimal heat stress metric for modelling heat-related mortality varies from country to country
Int. J. Climatol. 43 (12), 5553 - 5568
full text (doi) - Mukherjee, S., Mishra, A.K., Zscheischler, J., Entekhabi, D. (2023):
Interaction between dry and hot extremes at a global scale using a cascade modeling framework
Nat. Commun. 14 , art. 277
full text (doi) - Qian, C., Ye, Y., Bevacqua, E., Zscheischler, J. (2023):
Human influences on spatially compounding flooding and heatwave events in China and future increasing risks
Weather Clim. Extremes 42 , art. 100616
full text (doi) - Sweet, L.-B., Müller, C., Anand, M., Zscheischler, J. (2023):
Cross-validation strategy impacts the performance and interpretation of machine learning models
Artificial Intelligence for the Earth Systems (AIES) 2 (4), e230026
full text (doi) - Tschumi, E., Lienert, S., Bastos, A., Ciais, P., Gregor, K., Joos, F., Knauer, J., Papastefanou, P., Rammig, A., van der Wiel, K., Williams, K., Xu, Y., Zaehle, S., Zscheischler, J. (2023):
Large variability in simulated response of vegetation composition and carbon dynamics to variations in drought-heat occurrence
J. Geophys. Res.-Biogeosci. 128 (4), e2022JG007332
full text (doi) - van den Hurk, B.J.J.M., White, J.C., Ramos, A.M., Ward, P.J., Martius, O., Olbert, I., Roscoe, K., Goulart, H.M.D., Zscheischler, J. (2023):
Consideration of compound drivers and impacts in the disaster risk reduction cycle
iScience 26 (3), art. 106030
full text (doi) - Westra, S., Zscheischler, J. (2023):
Accounting for systemic complexity in the assessment of climate risk
One Earth 6 (6), 645 - 655
full text (doi) - Yang, H., Munson, S.M., Huntingford, C., Carvalhais, N., Knapp, A.K., Li, X., Peñuelas, J., Zscheischler, J., Chen, A. (2023):
The detection and attribution of extreme reductions in vegetation growth across the global land surface
Glob. Change Biol. 29 (8), 2351 - 2362
full text (doi) - Zhang, B., Wang, S., Zscheischler, J., Moradkhani, H. (2023):
Higher exposure of poorer people to emerging weather whiplash in a warmer world
Geophys. Res. Lett. 50 (21), e2023GL105640
full text (doi)
- Bevacqua, E., Zappa, G., Lehner, F., Zscheischler, J. (2022):
Precipitation trends determine future occurrences of compound hot–dry events
Nat. Clim. Chang. 12 (4), 350 - 355
full text (doi) - Boulaguiem, Y., Zscheischler, J., Vignotto, E., van der Wiel, K., Engelke, S. (2022):
Modeling and simulating spatial extremes by combining extreme value theory with generative adversarial networks
Environ. Data Sci. 1 , e5
full text (doi) - Cai, H., Liu, S., Shi, H., Zhou, Z., Jiang, S., Babovic, V. (2022):
Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method
J. Hydrol. 613, Part B , art. 128495
full text (doi) - Guo, X., Gao, Y., Zhang, S., Wu, L., Chang, P., Cai, W., Zscheischler, J., Leung, L.R., Small, J., Danabasoglu, G., Thompson, L., Gao, H. (2022):
Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model
Nat. Clim. Chang. 12 (2), 179 - 186
full text (doi) - Jiang, S. (2022):
oreopie/hydro-interpretive-dl: New implementation for European catchments (v0.3.0_pre)
Version: v0.3.0_pre ZENODO 10.5281/zenodo.4686106 - Jiang, S., Bevacqua, E., Zscheischler, J. (2022):
River flooding mechanisms and their changes in Europe revealed by explainable machine learning
Hydrol. Earth Syst. Sci. 26 (24), 6339 - 6359
full text (doi) - Le Grix, N., Zscheischler, J., Rodgers, K.B., Yamaguchi, R., Frölicher, T.L. (2022):
Hotspots and drivers of compound marine heatwaves and low net primary production extremes
Biogeosciences 19 (24), 5807 - 5835
full text (doi) - Li, D., Chen, Y., Messmer, M., Zhu, Y., Feng, J., Yin, B., Bevacqua, E. (2022):
Compound wind and precipitation extremes across the Indo-Pacific: Climatology, variability and drivers
Geophys. Res. Lett. 49 (14), e2022GL098594
full text (doi) - Li, J., Bevacqua, E., Chen, C., Wang, Z., Chen, X., Myneni, R.B., Wu, X., Xu, C.-Y., Zhang, Z., Zscheischler, J. (2022):
Regional asymmetry in the response of global vegetation growth to springtime compound climate events
Commun. Earth Environ. 3 , art. 123
full text (doi) - Maraun, D., Knevels, R., Mishra, A.N., Truhetz, H., Bevacqua, E., Proske, H., Zappa, G., Brenning, A., Petschko, H., Schaffer, A., Leopold, P., Puxley, B.L. (2022):
A severe landslide event in the Alpine foreland under possible future climate and land-use changes
Commun. Earth Environ. 3 , art. 87
full text (doi) - Maraun, D., Knevels, R., Mishra, A.N., Truhetz, H., Bevacqua, E., Proske, H., Zappa, G., Brenning, A., Petschko, H., Schaffer, A., Leopold, P., Puxley, B.L. (2022):
Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes [Data set]
ZENODO 10.5281/zenodo.6036814 - Marcolongo, A., Vladymyrov, M., Lienert, S., Peleg, N., Haug, S., Zscheischler, J. (2022):
Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
Environ. Data Sci. 1 , e2
full text (doi) - Orth, R., O, S., Zscheischler, J., Mahecha, M.D., Reichstein, M. (2022):
Contrasting biophysical and societal impacts of hydro-meteorological extremes
Environ. Res. Lett. 17 (1), art. 014044
full text (doi) - Ribeiro, A.F.S., Brando, P.M., Santos, L., Rattis, L., Hirschi, M., Hauser, M., Seneviratne, S.I., Zscheischler, J. (2022):
A compound event-oriented framework to tropical fire risk assessment in a changing climate
Environ. Res. Lett. 17 (6), art. 065015
full text (doi) - Switanek, M., Maraun, D., Bevacqua, E. (2022):
Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model
Int. J. Climatol. 42 (12), 6126 - 6147
full text (doi) - Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., Zscheischler, J. (2022):
A climate database with varying drought-heat signatures for climate impact modelling
Geosci. Data J. 9 (1), 154 - 166
full text (doi) - Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., Zscheischler, J. (2022):
The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition
Biogeosciences 19 (7), 1979 - 1993
full text (doi) - Tuel, A., Schaefli, B., Zscheischler, J., Martius, O. (2022):
On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe
Hydrol. Earth Syst. Sci. 26 (10), 2649 - 2669
full text (doi) - Xiong, R., Zheng, Y., Chen, N., Tian, Q., Liu, W., Han, F., Jiang, S., Lu, M., Zheng, Y. (2022):
Predicting dynamic riverine nitrogen export in unmonitored watersheds: Leveraging insights of AI from data-rich regions
Environ. Sci. Technol. 56 (14), 10530 - 10542
full text (doi) - Zscheischler, J., Lehner, F. (2022):
Attributing compound events to anthropogenic climate change
Bull. Amer. Meteorol. Soc. 103 (3), E936 - E953
full text (doi) - Zscheischler, J., Sillmann, J., Alexander, L. (2022):
Introduction to the special issue: Compound weather and climate events
Weather Clim. Extremes 35 , art. 100381
full text (doi)
- Bevacqua, E., De Michele, C., Manning, C., Couasnon, A., Ribeiro, A.F.S., Ramos, A.M., Vignotto, E., Bastos, A., Blesić, S., Durante, F., Hillier, J., Oliveira, S.C., Pinto, J.G., Ragno, E., Rivoire, P., Saunders, K., van der Wiel, K., Wu, W., Zhang, T., Zscheischler, J. (2021):
Guidelines for studying diverse types of compound weather and climate events
Earth Future 9 (11), e2021EF002340
full text (doi) - Gampe, D., Zscheischler, J., Reichstein, M., O’Sullivan, M., Smith, W.K., Sitch, S., Buermann, W. (2021):
Increasing impact of warm droughts on northern ecosystem productivity over recent decades
Nat. Clim. Chang. 11 (9), 772 - 779
full text (doi) - Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C.S., Frölicher, T.L. (2021):
Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period
Biogeosciences 18 (6), 2119 - 2137
full text (doi) - Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S.I., Horton, R. (2021):
Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields
Nat. Food 2 (9), 683 - 691
full text (doi) - Li, J., Wang, Z., Wu, X., Zscheischler, J., Guo, S., Chen, X. (2021):
A standardized index for assessing sub-monthly compound dry and hot conditions with application in China
Hydrol. Earth Syst. Sci. 25 (3), 1587 - 1601
full text (doi) - Messori, G., Bevacqua, E., Caballero, R., Coumou, D., De Luca, P., Faranda, D., Kornhuber, K., Martius, O., Pons, F., Raymond, C., Ye, K., Yiou, P., Zscheischler, J. (2021):
Compound climate events and extremes in the midlatitudes: Dynamics, simulation, and statistical characterization
Bull. Amer. Meteorol. Soc. 102 (4), E774 - E781
full text (doi) - Sillmann, J., Shepherd, T.G., van den Hurk, B., Hazeleger, W., Martius, O., Slingo, J., Zscheischler, J. (2021):
Event‐based storylines to address climate risk
Earth Future 9 (2), e2020EF001783
full text (doi) - Van de Walle, J., Thiery, W., Brogli, R., Martius, O., Zscheischler, J., van Lipzig, N.P.M. (2021):
Future intensification of precipitation and wind gust associated thunderstorms over Lake Victoria
Weather Clim. Extremes 34 , art. 100391
full text (doi) - Vignotto, E., Engelke, S., Zscheischler, J. (2021):
Clustering bivariate dependencies of compound precipitation and wind extremes over Great Britain and Ireland
Weather Clim. Extremes 32 , art. 100318
full text (doi) - Villalobos-Herrera, R., Bevacqua, E., Ribeiro, A.F.S., Auld, G., Crocetti, L., Mircheva, B., Ha, M., Zscheischler, J., De Michele, C. (2021):
Towards a compound-event-oriented climate model evaluation: a decomposition of the underlying biases in multivariate fire and heat stress hazards
Nat. Hazards Earth Syst. Sci. 21 (6), 1867 - 1885
full text (doi) - Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C.A., Tschumi, E., van der Wiel, K., Zhang, T., Zscheischler, J. (2021):
Identifying meteorological drivers of extreme impacts: an application to simulated crop yields
Earth Syst. Dynam. 12 (1), 151 - 172
full text (doi) - Whan, K., Zscheischler, J., Jordan, A.I., Ziegel, J.F. (2021):
Novel multivariate quantile mapping methods for ensemble post-processing of medium-range forecasts
Weather Clim. Extremes 32 , art. 100310
full text (doi) - Woolway, R.I., Kraemer, B.M., Zscheischler, J., Albergel, C. (2021):
Compound hot temperature and high chlorophyll extreme events in global lakes
Environ. Res. Lett. 16 (12), art. 124066
full text (doi) - Zscheischler, J., Naveau, P., Martius, O., Engelke, S., Raible, C.C. (2021):
Evaluating the dependence structure of compound precipitation and wind speed extremes
Earth Syst. Dynam. 12 (1), 1 - 16
full text (doi)