Articles | Volume 15, issue 3
https://doi.org/10.5194/esd-15-717-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-15-717-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jun 2024
Research article |
| 11 Jun 2024
| 11 Jun 2024
Intensified future heat extremes linked with increasing ecosystem water limitation
Jasper M. C. Denissen
CORRESPONDING AUTHOR
Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, the Netherlands
Research Department, European Centre for Medium-Range Weather Forecasts, Bonn, Germany
Adriaan J. Teuling
Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, the Netherlands
Sujan Koirala
Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Markus Reichstein
Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Gianpaolo Balsamo
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
World Meteorological Organization, Geneva, Switzerland
Martha M. Vogel
Red Cross Red Crescent Climate Centre, The Hague, the Netherlands
Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
René Orth
Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Related authors
No articles found.
Xueyan Cheng, Chunhui Zhan, Martin G. De Kauwe, Anke Hildebrandt, and Rene Orth
EGUsphere, https://doi.org/10.5194/egusphere-2026-1199, https://doi.org/10.5194/egusphere-2026-1199, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Using long-term satellite greenness data and a machine-learning attribution approach, we assessed vegetation responses to wet and dry soil moisture extremes and identified their dominant drivers. Dry extremes caused widespread negative greenness anomalies mainly controlled by pre-extreme conditions and seasonal timing, while wet extremes showed more heterogeneous responses additionally shaped by climate and topography, highlighting contrasting ecosystem sensitivity to hydrological extremes.
Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos
Atmos. Chem. Phys., 26, 2561–2595, https://doi.org/10.5194/acp-26-2561-2026, https://doi.org/10.5194/acp-26-2561-2026, 2026
Short summary
Short summary
We create a hybrid ecosystem-level carbon flux model using both eddy-covariance observations and observations of the atmospheric mole fraction of CO2 at three tall-tower observatories. Our study uses an atmospheric transport model (STILT) to connect the atmospheric signal to the ecosystem-level model. We show that this inclusion of atmospheric information meaningfully improves the model's representation of the interannual variability of the global net flux of CO2.
Gabriele Arduini, Christoph Rüdiger, and Gianpaolo Balsamo
The Cryosphere, 20, 1119–1137, https://doi.org/10.5194/tc-20-1119-2026, https://doi.org/10.5194/tc-20-1119-2026, 2026
Short summary
Short summary
Glaciers and ice sheets are critical components of the climate system. A set of improvements to snow and ice processes, targeting glacier and ice sheet regions, are introduced in a land surface model used to produce Weather Forecasts and climate reanalyses. The results demonstrate that more realistic snow and ice processes can lead to positive impacts on the simulation of various Earth System components, e.g. the surface temperature and the streamflow of rivers contributed by glacier melting.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Biogeosciences, 23, 767–792, https://doi.org/10.5194/bg-23-767-2026, https://doi.org/10.5194/bg-23-767-2026, 2026
Short summary
Short summary
We study when anthropogenic signal becomes detectable in the global land carbon sink, which has risen since the 1950s due to CO₂ fertilization and mid- to high-latitude warming. The signal emerges earlier at the global than at regional scales. Future scenarios (2016–2100) take longer to detect than the historical period (1851–2014) because the signal is weaker relative to larger natural variability. Removing circulation-induced variability with dynamical adjustment shortens the detection time.
Siyuan Wang, Hui Yang, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Claire Robin, Felix Cremer, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-670, https://doi.org/10.5194/essd-2025-670, 2025
Preprint under review for ESSD
Short summary
Short summary
Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as "regime" leave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.
Janneke O. E. Remmers, Rozemarijn ter Horst, Ehsan Nabavi, Ulrike Proske, Adriaan J. Teuling, Jeroen Vos, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 29, 5371–5382, https://doi.org/10.5194/hess-29-5371-2025, https://doi.org/10.5194/hess-29-5371-2025, 2025
Short summary
Short summary
Hydrological models are generally seen as neutral, despite acknowledged uncertainties. This notion has several, possibly harmful, consequences. In critical social sciences, non-neutrality in methods and results is an established topic of debate. We propose that in order to deal with it in hydrological modelling, the hydrological modelling network can learn from, and with, critical social sciences. The main lesson, from our perspective, is that responsible modelling is a shared responsibility.
Josephin Kroll, Ruth Stephan, Andrew F. Feldman, Diego G. Miralles, and Rene Orth
EGUsphere, https://doi.org/10.5194/egusphere-2025-4391, https://doi.org/10.5194/egusphere-2025-4391, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
In this study, we investigate contributors to and trends in the co-occurrence of heat and dryness. We find radiation, representing the atmospheric forcing, inducing high temperatures during dryness. For the persistence of heat, evaporation as the land contribution and the consequent effect on sensible heat flux becomes more important. While the co-occurrence of high temperatures and dryness shows a strong increase over 1980–2010, the atmospheric and land contributions show no clear trend.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-4082, https://doi.org/10.5194/egusphere-2025-4082, 2025
Short summary
Short summary
We used explainable machine learning that incorporates memory effects to study how plants respond to weather and drought. Using data from 90 sites worldwide, we show that memory plays a key role in regulating plant water stress. Forests and savannas rely on longer past conditions than grasslands, reflecting differences in rooting depth and water use. These insights improve our ability to anticipate ecosystem vulnerability as droughts intensify.
Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Hamdi, and Martin Jung
Biogeosciences, 22, 3965–3987, https://doi.org/10.5194/bg-22-3965-2025, https://doi.org/10.5194/bg-22-3965-2025, 2025
Short summary
Short summary
This study evaluates machine learning approaches for upscaling evapotranspiration from the site to the global scale. Sequential models capture temporal dynamics better, especially with precipitation data, but all models show biases in data-scarce regions. Improved upscaling requires richer training data, informed covariate selection, and physical constraints to enhance robustness and reduce extrapolation errors.
Theertha Kariyathan, Ana Bastos, Markus Reichstein, Wouter Peters, and Julia Marshall
Atmos. Chem. Phys., 25, 7863–7878, https://doi.org/10.5194/acp-25-7863-2025, https://doi.org/10.5194/acp-25-7863-2025, 2025
Short summary
Short summary
The carbon uptake period (CUP) is the time period when land absorbs more CO2 than it emits. While atmospheric CO2 mole fraction measurements can be used to assess CUP changes, atmospheric transport and asynchronous timing across regions reduce the accuracy of the estimates. Forward model experiments show that only ~ 50 % of prescribed shifts in CUP timing applied to surface fluxes (ΔCUPNEE) are captured in simulated CO2 mole fraction data at monitoring sites like the Barrow Atmospheric Baseline Observatory.
Hao Huang, Junguo Liu, Aifang Chen, Melissa Ruiz-Vásquez, and René Orth
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-376, https://doi.org/10.5194/essd-2025-376, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Hydrological research benefits from a growing number and diversity of datasets. However, the consistency across the increasing suite of datasets is unclear, limiting the comparability of findings derived from different datasets and variables. We find overall low consistency of numerous state-of-the-art precipitation, evapotranspiration, runoff, and soil moisture datasets in terms of the water balance. Meanwhile, the water balance consistency varies across space, sources, variables, and time.
Zavud Baghirov, Markus Reichstein, Basil Kraft, Bernhard Ahrens, Marco Körner, and Martin Jung
EGUsphere, https://doi.org/10.5194/egusphere-2025-3123, https://doi.org/10.5194/egusphere-2025-3123, 2025
Short summary
Short summary
We introduce a new global model that links how water and carbon move through land ecosystems. By combining process knowledge with artificial intelligence that learns from observations, we model daily changes in vegetation, water and carbon cycle processes. This model outperforms both purely data-driven and traditional process models, especially in dry and tropical regions. This advance could improve current understanding of water-carbon cycle relationships.
Laura Nadolski, Tarek S. El-Madany, Jacob Nelson, Arnaud Carrara, Gerardo Moreno, Richard Nair, Yunpeng Luo, Anke Hildebrandt, Victor Rolo, Markus Reichstein, and Sung-Ching Lee
Biogeosciences, 22, 2935–2958, https://doi.org/10.5194/bg-22-2935-2025, https://doi.org/10.5194/bg-22-2935-2025, 2025
Short summary
Short summary
Semi-arid ecosystems are crucial for Earth's carbon balance and are sensitive to changes in nitrogen (N) and phosphorus (P) levels. Their carbon dynamics are complex and not fully understood. We studied how long-term nutrient changes affect carbon exchange. In summer, the addition of N+P changed plant composition and productivity. In transitional seasons, carbon exchange was less weather-dependent with N. The addition of N and N+P increases carbon-exchange variability, driven by grass greenness.
Yigit Uckan, Melissa Ruiz-Vásquez, Kelley De Polt, and René Orth
Earth Syst. Dynam., 16, 869–889, https://doi.org/10.5194/esd-16-869-2025, https://doi.org/10.5194/esd-16-869-2025, 2025
Short summary
Short summary
We analyze drivers of hot temperature extremes, focusing on both atmospheric and land-surface factors. Using observation-based data and an analogue-based approach, we find that geopotential height at 500 hPa is the most significant driver worldwide at daily and weekly timescales. Surface net radiation and evaporative fraction play relevant roles at the daily scale and wind at the weekly timescale. The driver influence varies by region, related to differences in climate regimes and land cover.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
Short summary
Short summary
An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Marleen Pallandt, Marion Schrumpf, Holger Lange, Markus Reichstein, Lin Yu, and Bernhard Ahrens
Biogeosciences, 22, 1907–1928, https://doi.org/10.5194/bg-22-1907-2025, https://doi.org/10.5194/bg-22-1907-2025, 2025
Short summary
Short summary
As soils warm due to climate change, soil organic carbon (SOC) decomposes faster due to increased microbial activity, given sufficient available moisture. We modelled the microbial decomposition of plant litter and residue at different depths and found that deep soil layers are more sensitive than topsoils. Warming causes SOC loss, but its extent depends on the litter type and its temperature sensitivity, which can either counteract or amplify losses. Droughts may also counteract warming-induced SOC losses.
Devi Purnamasari, Adriaan J. Teuling, and Albrecht H. Weerts
Hydrol. Earth Syst. Sci., 29, 1483–1503, https://doi.org/10.5194/hess-29-1483-2025, https://doi.org/10.5194/hess-29-1483-2025, 2025
Short summary
Short summary
This paper introduces a method to identify irrigated areas by combining hydrology models with satellite temperature data. Our method was tested in the Rhine basin and aligns well with official statistics. It performs best in regions with large farms and less well in areas with small farms. Observed differences to existing data are influenced by data resolution and methods.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-365, https://doi.org/10.5194/egusphere-2025-365, 2025
Preprint archived
Short summary
Short summary
We developed a machine learning model that accounts for the memory effects of soil moisture and vegetation to predict Evaporative Fraction (EF) without relying on soil moisture as a direct input. The model accurately predicts EF during dry periods for the unseen sites, highlighting the key of meteorological memory effects. The learned memory effect related to rooting depth and soil water holding capacity could potentially serve as proxies for assessing the plant water stress.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Adriaan J. Teuling, Belle Holthuis, and Jasper F. D. Lammers
Hydrol. Earth Syst. Sci., 28, 3799–3806, https://doi.org/10.5194/hess-28-3799-2024, https://doi.org/10.5194/hess-28-3799-2024, 2024
Short summary
Short summary
The understanding of spatio-temporal variability of evapotranspiration (ET) is currently limited by a lack of measurement techniques that are low cost and that can be applied anywhere at any time. Here we show that evapotranspiration can be estimated accurately using observations made by smartphone sensors, suggesting that smartphone-based ET monitoring could provide a realistic and low-cost alternative for real-time ET estimation in the field.
Charles Nduhiu Wamucii, Pieter R. van Oel, Adriaan J. Teuling, Arend Ligtenberg, John Mwangi Gathenya, Gert Jan Hofstede, Meine van Noordwijk, and Erika N. Speelman
Hydrol. Earth Syst. Sci., 28, 3495–3518, https://doi.org/10.5194/hess-28-3495-2024, https://doi.org/10.5194/hess-28-3495-2024, 2024
Short summary
Short summary
The study explored the role of serious gaming in strengthening stakeholder engagement in addressing human–water challenges. The gaming approach guided community discussions toward implementable decisions. The results showed increased active participation, knowledge gain, and use of plural pronouns. We observed decreased individual interests and conflicts among game participants. The study presents important implications for creating a collective basis for water resources management.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
Short summary
Short summary
Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Martin Jung, Jacob Nelson, Mirco Migliavacca, Tarek El-Madany, Dario Papale, Markus Reichstein, Sophia Walther, and Thomas Wutzler
Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, https://doi.org/10.5194/bg-21-1827-2024, 2024
Short summary
Short summary
We present a methodology to detect inconsistencies in perhaps the most important data source for measurements of ecosystem–atmosphere carbon, water, and energy fluxes. We expect that the derived consistency flags will be relevant for data users and will help in improving our understanding of and our ability to model ecosystem–climate interactions.
Prajwal Khanal, Anne J. Hoek Van Dijke, Timo Schaffhauser, Wantong Li, Sinikka J. Paulus, Chunhui Zhan, and René Orth
Biogeosciences, 21, 1533–1547, https://doi.org/10.5194/bg-21-1533-2024, https://doi.org/10.5194/bg-21-1533-2024, 2024
Short summary
Short summary
Water availability is essential for vegetation functioning, but the depth of vegetation water uptake is largely unknown due to sparse ground measurements. This study correlates vegetation growth with soil moisture availability globally to infer vegetation water uptake depth using only satellite-based data. We find that the vegetation water uptake depth varies across climate regimes and vegetation types and also changes during dry months at a global scale.
Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, and Ana Bastos
Atmos. Chem. Phys., 24, 2555–2582, https://doi.org/10.5194/acp-24-2555-2024, https://doi.org/10.5194/acp-24-2555-2024, 2024
Short summary
Short summary
Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Awad M. Ali, Lieke A. Melsen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 27, 4057–4086, https://doi.org/10.5194/hess-27-4057-2023, https://doi.org/10.5194/hess-27-4057-2023, 2023
Short summary
Short summary
Using a new approach based on a combination of modeling and Earth observation, useful information about the filling of the Grand Ethiopian Renaissance Dam can be obtained with limited data and proper rainfall selection. While the monthly streamflow into Sudan has decreased significantly (1.2 × 109–5 × 109 m3) with respect to the non-dam scenario, the negative impact has been masked due to higher-than-average rainfall. We reveal that the dam will need 3–5 more years to complete filling.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
Preprint archived
Short summary
Short summary
We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Marleen R. Lam, Alessia Matanó, Anne F. Van Loon, Rhoda A. Odongo, Aklilu D. Teklesadik, Charles N. Wamucii, Marc J. C. van den Homberg, Shamton Waruru, and Adriaan J. Teuling
Nat. Hazards Earth Syst. Sci., 23, 2915–2936, https://doi.org/10.5194/nhess-23-2915-2023, https://doi.org/10.5194/nhess-23-2915-2023, 2023
Short summary
Short summary
There is still no full understanding of the relation between drought impacts and drought indices in the Horn of Africa where water scarcity and arid regions are also present. This study assesses their relation in Kenya. A random forest model reveals that each region, aggregated by aridity, has its own set of predictors for every impact category. Water scarcity was not found to be related to aridity. Understanding these relations contributes to the development of drought early warning systems.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
Short summary
Short summary
The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
Short summary
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
Short summary
Short summary
A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Manal Lam'barki, Wantong Li, Sungmin O, Chunhui Zhan, and Rene Orth
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-404, https://doi.org/10.5194/hess-2022-404, 2022
Manuscript not accepted for further review
Short summary
Short summary
We investigate the main drivers of high river flows in near-natural European catchments. While there are a lot of previous research in this area, the understanding of the relative relevance of high flow drivers other than precipitation is understudied. We find that the secondary drivers of high river flows are very diverse and become more relevant for more extreme events. This illustrates the necessity of flood management by considering a multitude of drivers in the context of climate change.
Sinikka Jasmin Paulus, Tarek Sebastian El-Madany, René Orth, Anke Hildebrandt, Thomas Wutzler, Arnaud Carrara, Gerardo Moreno, Oscar Perez-Priego, Olaf Kolle, Markus Reichstein, and Mirco Migliavacca
Hydrol. Earth Syst. Sci., 26, 6263–6287, https://doi.org/10.5194/hess-26-6263-2022, https://doi.org/10.5194/hess-26-6263-2022, 2022
Short summary
Short summary
In this study, we analyze small inputs of water to ecosystems such as fog, dew, and adsorption of vapor. To measure them, we use a scaling system and later test our attribution of different water fluxes to weight changes. We found that they occur frequently during 1 year in a dry summer ecosystem. In each season, a different flux seems dominant, but they all mainly occur during the night. Therefore, they could be important for the biosphere because rain is unevenly distributed over the year.
Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Earth Syst. Dynam., 13, 1505–1533, https://doi.org/10.5194/esd-13-1505-2022, https://doi.org/10.5194/esd-13-1505-2022, 2022
Short summary
Short summary
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
Short summary
Short summary
Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
Short summary
Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
Short summary
Short summary
Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Alessandro Montemagno, Christophe Hissler, Victor Bense, Adriaan J. Teuling, Johanna Ziebel, and Laurent Pfister
Biogeosciences, 19, 3111–3129, https://doi.org/10.5194/bg-19-3111-2022, https://doi.org/10.5194/bg-19-3111-2022, 2022
Short summary
Short summary
We investigated the biogeochemical processes that dominate the release and retention of elements (nutrients and potentially toxic elements) during litter degradation. Our results show that toxic elements are retained in the litter, while nutrients are released in solution during the first stages of degradation. This seems linked to the capability of trees to distribute the elements between degradation-resistant and non-degradation-resistant compounds of leaves according to their chemical nature.
Linqi Zhang, Yi Liu, Liliang Ren, Adriaan J. Teuling, Ye Zhu, Linyong Wei, Linyan Zhang, Shanhu Jiang, Xiaoli Yang, Xiuqin Fang, and Hang Yin
Hydrol. Earth Syst. Sci., 26, 3241–3261, https://doi.org/10.5194/hess-26-3241-2022, https://doi.org/10.5194/hess-26-3241-2022, 2022
Short summary
Short summary
In this study, three machine learning methods displayed a good detection capacity of flash droughts. The RF model was recommended to estimate the depletion rate of soil moisture and simulate flash drought by considering the multiple meteorological variable anomalies in the adjacent time to drought onset. The anomalies of precipitation and potential evapotranspiration exhibited a stronger synergistic but asymmetrical effect on flash droughts compared to slowly developing droughts.
Femke A. Jansen, Remko Uijlenhoet, Cor M. J. Jacobs, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 26, 2875–2898, https://doi.org/10.5194/hess-26-2875-2022, https://doi.org/10.5194/hess-26-2875-2022, 2022
Short summary
Short summary
We studied the controls on open water evaporation with a focus on Lake IJssel, the Netherlands, by analysing eddy covariance observations over two summer periods at two locations at the borders of the lake. Wind speed and the vertical vapour pressure gradient can explain most of the variation in observed evaporation, which is in agreement with Dalton's model. We argue that the distinct characteristics of inland waterbodies need to be taken into account when parameterizing their evaporation.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
Short summary
Short summary
The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
Short summary
Short summary
We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Josephin Kroll, Jasper M. C. Denissen, Mirco Migliavacca, Wantong Li, Anke Hildebrandt, and Rene Orth
Biogeosciences, 19, 477–489, https://doi.org/10.5194/bg-19-477-2022, https://doi.org/10.5194/bg-19-477-2022, 2022
Short summary
Short summary
Plant growth relies on having access to energy (solar radiation) and water (soil moisture). This energy and water availability is impacted by weather extremes, like heat waves and droughts, which will occur more frequently in response to climate change. In this context, we analysed global satellite data to detect in which regions extreme plant growth is controlled by energy or water. We find that extreme plant growth is associated with temperature- or soil-moisture-related extremes.
Charles Nduhiu Wamucii, Pieter R. van Oel, Arend Ligtenberg, John Mwangi Gathenya, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 25, 5641–5665, https://doi.org/10.5194/hess-25-5641-2021, https://doi.org/10.5194/hess-25-5641-2021, 2021
Short summary
Short summary
East African water towers (WTs) are under pressure from human influences within and without, but the water yield (WY) is more sensitive to climate changes from within. Land use changes have greater impacts on WY in the surrounding lowlands. The WTs have seen a strong shift towards wetter conditions while, at the same time, the potential evapotranspiration is gradually increasing. The WTs were identified as non-resilient, and future WY may experience more extreme variations.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
Short summary
Short summary
Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
Christopher Krich, Mirco Migliavacca, Diego G. Miralles, Guido Kraemer, Tarek S. El-Madany, Markus Reichstein, Jakob Runge, and Miguel D. Mahecha
Biogeosciences, 18, 2379–2404, https://doi.org/10.5194/bg-18-2379-2021, https://doi.org/10.5194/bg-18-2379-2021, 2021
Short summary
Short summary
Ecosystems and the atmosphere interact with each other. These interactions determine e.g. the water and carbon fluxes and thus are crucial to understand climate change effects. We analysed the interactions for many ecosystems across the globe, showing that very different ecosystems can have similar interactions with the atmosphere. Meteorological conditions seem to be the strongest interaction-shaping factor. This means that common principles can be identified to describe ecosystem behaviour.
Joost Buitink, Lieke A. Melsen, and Adriaan J. Teuling
Earth Syst. Dynam., 12, 387–400, https://doi.org/10.5194/esd-12-387-2021, https://doi.org/10.5194/esd-12-387-2021, 2021
Short summary
Short summary
Higher temperatures influence both evaporation and snow processes. These two processes have a large effect on discharge but have distinct roles during different seasons. In this study, we study how higher temperatures affect the discharge via changed evaporation and snow dynamics. Higher temperatures lead to enhanced evaporation but increased melt from glaciers, overall lowering the discharge. During the snowmelt season, discharge was reduced further due to the earlier depletion of snow.
Cited articles
Albergel, C., Dorigo, W., Reichle, R. H., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., and Wagner, W.: Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing, J. Hydrometeorol., 14, 1259–1277, https://doi.org/10.1175/JHM-D-12-0161.1, 2013.
Anderegg, W. R. L., Kane, J. M., and Anderegg, L. D. L.: Consequences of widespread tree mortality triggered by drought and temperature stress, Nat. Clim. Change, 3, 30–36, https://doi.org/10.1038/nclimate1635, 2013.
Berg, A. and Sheffield, J.: Climate Change and Drought: the Soil Moisture Perspective, Curr. Clim. Change Rep., 4, 180–191, https://doi.org/10.1007/s40641-018-0095-0, 2018.
Berg, A., Sheffield, J., and Milly, P. C. D.: Divergent surface and total soil moisture projections under global warming, Geophys. Res. Lett., 44, 236–244, https://doi.org/10.1002/2016GL071921, 2017.
Budyko, M. I.: Climate and life, Academic Press, ISBN 978-0-12-139450-9, 1974.
Buzan, J. R. and Huber, M.: Moist Heat Stress on a Hotter Earth, Annu. Rev. Earth Planet. Sci., 48, 623–655, https://doi.org/10.1146/annurev-earth-053018-060100, 2020.
Cassou, C., Terray, L., and Phillips, A. S.: Tropical Atlantic Influence on European Heat Waves, J. Climate, 18, 2805–2811, https://doi.org/10.1175/JCLI3506.1, 2005.
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Syst., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019.
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman G., Wang, F., Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell, G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S., Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Future Gener. Comp. Sy., 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014 (data available at: https://aims2.llnl.gov/search/?project=CMIP6/, last access: 4 June 2024).
De Kauwe, M. G., Medlyn, B. E., Walker, A. P., Zaehle, S., Asao, S., Guenet, B., Harper, A. B., Hickler, T., Jain, A. K., Luo, Y., Lu, X., Luus, K., Parton, W. J., Shu, S., Wang, Y.-P., Werner, C., Xia, J., Pendall, E., Morgan, J. A., Ryan, E. M., Carrillo, Y., Dijkstra, F. A., Zelikova, T. J., and Norby, R. J.: Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment, Glob. Change Biol., 23, 3623–3645, https://doi.org/10.1111/gcb.13643, 2017.
Denissen, J.: Data_Denissen_et_al_2024_ESD, Zenodo [data set], https://doi.org/10.5281/zenodo.11072827, 2024a.
Denissen, J. M. C.: JasperMCDenissen/Denissen_etal_2024_ESD: Denissen_et_al_2024_ESD (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.11073163, 2024b.
Denissen, J. M. C., Teuling, A. J., Reichstein, M., and Orth, R.: Critical Soil Moisture Derived From Satellite Observations Over Europe, J. Geophys. Res.-Atmos., 125, e2019JD031672, https://doi.org/10.1029/2019JD031672, 2020.
Denissen, J. M. C., Orth, R., Wouters, H., Miralles, D. G., van Heerwaarden, C. C., Vilà-Guerau de Arellano, J., and Teuling, A. J.: Soil moisture signature in global weather balloon soundings, Npj Clim. Atmos. Sci., 4, 1–8, https://doi.org/10.1038/s41612-021-00167-w, 2021.
Denissen, J. M. C., Teuling, A. J., Pitman, A. J., Koirala, S., Migliavacca, M., Li, W., Reichstein, M., Winkler, A. J., Zhan, C., and Orth, R.: Widespread shift from ecosystem energy to water limitation with climate change, Nat. Clim. Change, 12, 677–684, https://doi.org/10.1038/s41558-022-01403-8, 2022.
Dirmeyer, P. A., Balsamo, G., Blyth, E. M., Morrison, R., and Cooper, H. M.: Land-Atmosphere Interactions Exacerbated the Drought and Heatwave Over Northern Europe During Summer 2018, AGU Adv., 2, e2020AV000283, https://doi.org/10.1029/2020AV000283, 2021.
Donat, M. G., Pitman, A. J., and Seneviratne, S. I.: Regional warming of hot extremes accelerated by surface energy fluxes, Geophys. Res. Lett., 44, 7011–7019, https://doi.org/10.1002/2017GL073733, 2017.
Donohue, R. J., Roderick, M. L., McVicar, T. R., and Farquhar, G. D.: Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments, Geophys. Res. Lett., 40, 3031–3035, https://doi.org/10.1002/grl.50563, 2013.
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall Coupled Model Description and Simulation Characteristics, J. Adv. Model. Earth Syst., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020.
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4702, 2021a.
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.15636, 2021b.
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.
Feldman, A. F., Short Gianotti, D. J., Dong, J., Trigo, I. F., Salvucci, G. D., and Entekhabi, D.: Tropical surface temperature response to vegetation cover changes and the role of drylands, Glob. Change Biol., 29, 110–125, https://doi.org/10.1111/gcb.16455, 2023.
Fischer, E. M., Seneviratne, S. I., Lüthi, D., and Schär, C.: Contribution of land-atmosphere coupling to recent European summer heat waves, Geophys. Res. Lett., 34, L06707, https://doi.org/10.1029/2006GL029068, 2007.
Good, P.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10901, 2020.
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Kuhlbrodt, T., and Walton, J.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1567, 2019.
Goulart, H. M. D., van der Wiel, K., Folberth, C., Balkovic, J., and van den Hurk, B.: Storylines of weather-induced crop failure events under climate change, Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, 2021.
Greve, P., Roderick, M. L., Ukkola, A. M., and Wada, Y.: The aridity Index under global warming, Environ. Res. Lett., 14, 124006, https://doi.org/10.1088/1748-9326/ab5046, 2019.
Harrington, L. J., Otto, F. E. L., Cowan, T., and Hegerl, G. C.: Circulation analogues and uncertainty in the time-evolution of extreme event probabilities: evidence from the 1947 Central European heatwave, Clim. Dynam., 53, 2229–2247, https://doi.org/10.1007/s00382-019-04820-2, 2019.
Hassan, T.: acccmip6: Python package for accessing and downloading CMIP6 data (v2.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.6559056, 2022.
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Clim. Change, 109, 117, https://doi.org/10.1007/s10584-011-0153-2, 2011.
Jézéquel, A., Cattiaux, J., Naveau, P., Radanovics, S., Ribes, A., Vautard, R., Vrac, M., and Yiou, P.: Trends of atmospheric circulation during singular hot days in Europe, Environ. Res. Lett., 13, 054007, https://doi.org/10.1088/1748-9326/aab5da, 2018.
John, J. G., Blanton, C., McHugh, C., Radhakrishnan, A., Rand, K., Vahlenkamp, H., Wilson, C., Zadeh, N. T., Dunne, J. P., Dussin, R., Horowitz, L. W., Krasting, J. P., Lin, P., Malyshev, S., Naik, V., Ploshay, J., Shevliakova, E., Silvers, L., Stock, C., Winton, M., and Zeng, Y.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8706, 2018.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74, https://doi.org/10.1038/s41597-019-0076-8, 2019.
Jungclaus, J., Bittner, M., Wieners, K.-H., Wachsmann, F., Schupfner, M., Legutke, S., Giorgetta, M., Reick, C., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Esch, M., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6594, 2019.
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft, A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W., Lin, P., Milly, P. C. D., Shevliakova, E., Stock, C., Winton, M., Wittenberg, A. T., Xie, Y., and Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8597, 2018.
Lemordant, L. and Gentine, P.: Vegetation Response to Rising CO2 Impacts Extreme Temperatures, Geophys. Res. Lett., 46, 1383–1392, https://doi.org/10.1029/2018GL080238, 2019.
Lorenz, R., Argüeso, D., Donat, M. G., Pitman, A. J., van den Hurk, B., Berg, A., Lawrence, D. M., Chéruy, F., Ducharne, A., Hagemann, S., Meier, A., Milly, P. C. D., and Seneviratne, S. I.: Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble, J. Geophys. Res.-Atmos., 121, 607–623, https://doi.org/10.1002/2015JD024053, 2016.
Lovato, T. and Peano, D.: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3825, 2020a.
Lovato, T. and Peano, D.: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1365, 2020b.
Martínez-de la Torre, A., Blyth, E. M., and Robinson, E. L.: Evaluation of Drydown Processes in Global Land Surface and Hydrological Models Using Flux Tower Evapotranspiration, Water, 11, 356, https://doi.org/10.3390/w11020356, 2019.
Matthews, T.: Humid heat and climate change, Prog. Phys. Geogr. Earth Environ., 42, 391–405, https://doi.org/10.1177/0309133318776490, 2018.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019.
McDowell, N. G. and Allen, C. D.: Darcy's law predicts widespread forest mortality under climate warming, Nat. Clim. Change, 5, 669–672, https://doi.org/10.1038/nclimate2641, 2015.
Miralles, D. G., van den Berg, M. J., Teuling, A. J., and de Jeu, R. A. M.: Soil moisture-temperature coupling: A multiscale observational analysis, Geophys. Res. Lett., 39, L21707, https://doi.org/10.1029/2012GL053703, 2012.
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and Vilà-Guerau de Arellano, J.: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation, Nat. Geosci., 7, 345–349, https://doi.org/10.1038/ngeo2141, 2014.
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J. Adv. Model. Earth Syst., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018.
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 2001 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/CDS.68D2BB30, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nabat, P., Somot, S., Mallet, M., Sanchez-Lorenzo, A., and Wild, M.: Contribution of anthropogenic sulfate aerosols to the changing Euro-Mediterranean climate since 1980, Geophys. Res. Lett., 41, 5605–5611, https://doi.org/10.1002/2014GL060798, 2014.
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999, Science, 300, 1560–1563, https://doi.org/10.1126/science.1082750, 2003.
O, S. and Orth, R.: Global soil moisture data derived through machine learning trained with in-situ measurements, Sci. Data, 8, 170, https://doi.org/10.1038/s41597-021-00964-1, 2021.
O, S., Hou, X., and Orth, R.: Observational evidence of wildfire-promoting soil moisture anomalies, Sci. Rep., 10, 11008, https://doi.org/10.1038/s41598-020-67530-4, 2020.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Orth, R., O, S., Zscheischler, J., Mahecha, M. D., and Reichstein, M.: Contrasting biophysical and societal impacts of hydro-meteorological extremes, Environ. Res. Lett., 17, 014044, https://doi.org/10.1088/1748-9326/ac4139, 2022.
Qian, Y., Leung, L. R., Ghan, S. J., and Giorgi, F.: Regional climate effects of aerosols over China: modeling and observation, Tellus B, 55, 914–934, https://doi.org/10.3402/tellusb.v55i4.16379, 2011.
Quesada, B., Vautard, R., Yiou, P., Hirschi, M., and Seneviratne, S. I.: Asymmetric European summer heat predictability from wet and dry southern winters and springs, Nat. Clim. Change, 2, 736–741, https://doi.org/10.1038/nclimate1536, 2012.
Rasmijn, L. M., van der Schrier, G., Bintanja, R., Barkmeijer, J., Sterl, A., and Hazeleger, W.: Future equivalent of 2010 Russian heatwave intensified by weakening soil moisture constraints, Nat. Clim. Change, 8, 381–385, https://doi.org/10.1038/s41558-018-0114-0, 2018.
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6109, 2019.
Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias, N., Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.-L., Russo, A., and Belhadj-Khedher, C.: Increased likelihood of heat-induced large wildfires in the Mediterranean Basin, Sci. Rep., 10, 13790, https://doi.org/10.1038/s41598-020-70069-z, 2020.
Schumacher, D. L., Keune, J., van Heerwaarden, C. C., Vilà-Guerau de Arellano, J., Teuling, A. J., and Miralles, D. G.: Amplification of mega-heatwaves through heat torrents fuelled by upwind drought, Nat. Geosci., 12, 712–717, https://doi.org/10.1038/s41561-019-0431-6, 2019.
Schupfner, M., Wieners, K.-H., Wachsmann, F., Steger, C., Bittner, M., Jungclaus, J., Früh, B., Pankatz, K., Giorgetta, M., Reick, C., Legutke, S., Esch, M., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4403, 2019.
Schwaab, J., Meier, R., Mussetti, G., Seneviratne, S., Bürgi, C., and Davin, E. L.: The role of urban trees in reducing land surface temperatures in European cities, Nat. Commun., 12, 6763, https://doi.org/10.1038/s41467-021-26768-w, 2021.
Schwingshackl, C., Hirschi, M., and Seneviratne, S. I.: A theoretical approach to assess soil moisture–climate coupling across CMIP5 and GLACE-CMIP5 experiments, Earth Syst. Dynam., 9, 1217–1234, https://doi.org/10.5194/esd-9-1217-2018, 2018.
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4068, 2018.
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y-Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Syst., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Syst., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Seneviratne, S. I., Lüthi, D., Litschi, M., and Schär, C.: Land–atmosphere coupling and climate change in Europe, Nature, 443, 205–209, https://doi.org/10.1038/nature05095, 2006.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Seneviratne, S. I., Donat, M. G., Mueller, B., and Alexander, L. V.: No pause in the increase of hot temperature extremes, Nat. Clim. Change, 4, 161–163, https://doi.org/10.1038/nclimate2145, 2014.
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., and Zhou, B.: Weather and Climate Extreme Events in a Changing Climate, 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, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021.
Sillmann, J., Thorarinsdottir, T., Keenlyside, N., Schaller, N., Alexander, L. V., Hegerl, G., Seneviratne, S. I., Vautard, R., Zhang, X., and Zwiers, F. W.: Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities, Weather Clim. Extrem., 18, 65–74, https://doi.org/10.1016/j.wace.2017.10.003, 2017.
Sippel, S., Zscheischler, J., Mahecha, M. D., Orth, R., Reichstein, M., Vogel, M., and Seneviratne, S. I.: Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics, Earth Syst. Dynam., 8, 387–403, https://doi.org/10.5194/esd-8-387-2017, 2017.
Stegehuis, A. I., Vogel, M. M., Vautard, R., Ciais, P., Teuling, A. J., and Seneviratne, S. I.: Early Summer Soil Moisture Contribution to Western European Summer Warming, J. Geophys. Res.-Atmos., 126, e2021JD034646, https://doi.org/10.1029/2021JD034646, 2021.
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, 2019.
Teuling, A. J.: A hot future for European droughts, Nat. Clim. Change, 8, 364–365, https://doi.org/10.1038/s41558-018-0154-5, 2018.
Teuling, A. J., Seneviratne, S. I., Stöckli, R., Reichstein, M., Moors, E., Ciais, P., Luyssaert, S., van den Hurk, B., Ammann, C., Bernhofer, C., Dellwik, E., Gianelle, D., Gielen, B., Grünwald, T., Klumpp, K., Montagnani, L., Moureaux, C., Sottocornola, M., and Wohlfahrt, G.: Contrasting response of European forest and grassland energy exchange to heatwaves, Nat. Geosci., 3, 722–727, https://doi.org/10.1038/ngeo950, 2010.
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Henri Theil's Contributions to Economics and Econometrics: Econometric Theory and Methodology, edited by: Raj, B. and Koerts, J., Springer Netherlands, Dordrecht, 345–381, https://doi.org/10.1007/978-94-011-2546-8_20, 1992.
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.
Trenberth, K. E., Fasullo, J. T., and Shepherd, T. G.: Attribution of climate extreme events, Nat. Clim. Change, 5, 725–730, https://doi.org/10.1038/nclimate2657, 2015.
Ukkola, A. M., Kauwe, M. G. D., Pitman, A. J., Best, M. J., Abramowitz, G., Haverd, V., Decker, M., and Haughton, N.: Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts, Environ. Res. Lett., 11, 104012, https://doi.org/10.1088/1748-9326/11/10/104012, 2016a.
Ukkola, A. M., Prentice, I. C., Keenan, T. F., van Dijk, A. I. J. M., Viney, N. R., Myneni, R. B., and Bi, J.: Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation, Nat. Clim. Change, 6, 75–78, https://doi.org/10.1038/nclimate2831, 2016b.
Ukkola, A. M., Pitman, A. J., Donat, M. G., De Kauwe, M. G., and Angélil, O.: Evaluating the Contribution of Land-Atmosphere Coupling to Heat Extremes in CMIP5 Models, Geophys. Res. Lett., 45, 9003–9012, https://doi.org/10.1029/2018GL079102, 2018.
Vogel, M. M., Orth, R., Cheruy, F., Hagemann, S., Lorenz, R., van den Hurk, B. J. J. M., and Seneviratne, S. I.: Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks, Geophys. Res. Lett., 44, 1511–1519, https://doi.org/10.1002/2016GL071235, 2017.
Vogel, M. M., Zscheischler, J., and Seneviratne, S. I.: Varying soil moisture–atmosphere feedbacks explain divergent temperature extremes and precipitation projections in central Europe, Earth Syst. Dynam., 9, 1107–1125, https://doi.org/10.5194/esd-9-1107-2018, 2018.
Vogel, M. M., Zscheischler, J., Wartenburger, R., Dee, D., and Seneviratne, S. I.: Concurrent 2018 Hot Extremes Across Northern Hemisphere Due to Human-Induced Climate Change, Earths Future, 7, 692–703, https://doi.org/10.1029/2019EF001189, 2019.
Vogel, M. M., Hauser, M., and Seneviratne, S. I.: Projected changes in hot, dry and wet extreme events' clusters in CMIP6 multi-model ensemble, Environ. Res. Lett., 15, 094021, https://doi.org/10.1088/1748-9326/ab90a7, 2020.
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, 2018.
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4224, 2019a.
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4226, 2019b.
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1, J. Adv. Model. Earth Syst., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019.
Walker, A. P., De Kauwe, M. G., Bastos, A., Belmecheri, S., Georgiou, K., Keeling, R. F., McMahon, S. M., Medlyn, B. E., Moore, D. J. P., Norby, R. J., Zaehle, S., Anderson-Teixeira, K. J., Battipaglia, G., Brienen, R. J. W., Cabugao, K. G., Cailleret, M., Campbell, E., Canadell, J. G., Ciais, P., Craig, M. E., Ellsworth, D. S., Farquhar, G. D., Fatichi, S., Fisher, J. B., Frank, D. C., Graven, H., Gu, L., Haverd, V., Heilman, K., Heimann, M., Hungate, B. A., Iversen, C. M., Joos, F., Jiang, M., Keenan, T. F., Knauer, J., Körner, C., Leshyk, V. O., Leuzinger, S., Liu, Y., MacBean, N., Malhi, Y., McVicar, T. R., Penuelas, J., Pongratz, J., Powell, A. S., Riutta, T., Sabot, M. E. B., Schleucher, J., Sitch, S., Smith, W. K., Sulman, B., Taylor, B., Terrer, C., Torn, M. S., Treseder, K. K., Trugman, A. T., Trumbore, S. E., van Mantgem, P. J., Voelker, S. L., Whelan, M. E., and Zuidema, P. A.: Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2, New Phytol., 229, 2413–2445, https://doi.org/10.1111/nph.16866, 2021.
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Legutke, S., Schupfner, M., Wachsmann, F., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6595, 2019a.
Wieners, K.-H., Giorgetta, M., Jungclaus, J., Reick, C., Esch, M., Bittner, M., Gayler, V., Haak, H., de Vrese, P., Raddatz, T., Mauritsen, T., von Storch, J.-S., Behrens, J., Brovkin, V., Claussen, M., Crueger, T., Fast, I., Fiedler, S., Hagemann, S., Hohenegger, C., Jahns, T., Kloster, S., Kinne, S., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Müller, W., Nabel, J., Notz, D., Peters-von Gehlen, K., Pincus, R., Pohlmann, H., Pongratz, J., Rast, S., Schmidt, H., Schnur, R., Schulzweida, U., Six, K., Stevens, B., Voigt, A., and Roeckner, E.: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6705, 2019b.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model. Earth Syst., 10, 357–380, https://doi.org/10.1002/2017MS001115, 2018.
Wouters, H., Keune, J., Petrova, I. Y., van Heerwaarden, C. C., Teuling, A. J., Pal, J. S., Vilà-Guerau de Arellano, J., and Miralles, D. G.: Soil drought can mitigate deadly heat stress thanks to a reduction of air humidity, Sci. Adv., 8, eabe6653, https://doi.org/10.1126/sciadv.abe6653, 2022.
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2948, 2018.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6, Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.
Xin, X., Wu, T., Shi, X., Zhang, F., Li, J., Chu, M., Liu, Q., Yan, J., Ma, Q., and Wei, M.: BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP ssp370, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3035, 2019.
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6842, 2019a.
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6929, 2019b.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteorol. Soc. Jpn. Ser. II, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019c.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016.
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4272, 2019a.
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4333, 2019b.
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., Srbinovsky, J., Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System Model: ACCESS-ESM1.5, J. South. Hemisphere Earth Syst. Sci., 70, 193–214, https://doi.org/10.1071/ES19035, 2020.
Short summary
Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly introduced by large-scale atmospheric circulation but can be modulated by vegetation. Vegetation with access to water uses solar energy to evaporate water into the atmosphere. Under dry conditions, water may not be available, suppressing evaporation and heating the atmosphere. Using climate projections, we show that regionally less water is available for vegetation, intensifying future heat extremes.
Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly...
Altmetrics
Final-revised paper
Preprint