Articles | Volume 15, issue 2
https://doi.org/10.5194/esd-15-167-2024
© 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-15-167-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of groundwater representation on heat events in regional climate simulations over Europe
Liubov Poshyvailo-Strube
CORRESPONDING AUTHOR
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
Niklas Wagner
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
Klaus Goergen
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
Carina Furusho-Percot
Department of Agroecosystem, AgroClim Unit, National Research Institute for Agriculture, Food and Environment (INRAE), Avignon, France
Carl Hartick
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Jülich, Germany
Stefan Kollet
Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
Related authors
Liubov Poshyvailo-Strube, Rolf Müller, Stephan Fueglistaler, Michaela I. Hegglin, Johannes C. Laube, C. Michael Volk, and Felix Ploeger
Atmos. Chem. Phys., 22, 9895–9914, https://doi.org/10.5194/acp-22-9895-2022, https://doi.org/10.5194/acp-22-9895-2022, 2022
Short summary
Short summary
Brewer–Dobson circulation (BDC) controls the composition of the stratosphere, which in turn affects radiation and climate. As the BDC cannot be measured directly, it is necessary to infer its strength and trends indirectly. In this study, we test in the
model worlddifferent methods for estimating the mean age of air trends based on a combination of stratospheric water vapour and methane data. We also provide simple practical advice of a more reliable estimation of the mean age of air trends.
Liubov Poshyvailo, Rolf Müller, Paul Konopka, Gebhard Günther, Martin Riese, Aurélien Podglajen, and Felix Ploeger
Atmos. Chem. Phys., 18, 8505–8527, https://doi.org/10.5194/acp-18-8505-2018, https://doi.org/10.5194/acp-18-8505-2018, 2018
Short summary
Short summary
Water vapour (H2O) in the UTLS is a key player for global radiation, which is critical for predictions of future climate change. We investigate the effects of current uncertainties in tropopause temperature, horizontal transport and small-scale mixing on simulated H2O, using the Chemical Lagrangian Model of the Stratosphere. Our sensitivity studies provide new insights into the leading processes controlling stratospheric H2O, important for assessing and improving climate model projections.
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.
Bamidele Joseph Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3132, https://doi.org/10.5194/egusphere-2023-3132, 2024
Short summary
Short summary
This study uses simulations to understand how the soil information across Africa affects the water balance, using 4 soil databases and 3 different rainfall datasets. Results show that the soil information impacts water balance estimates, especially with a higher rate of rainfall.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1460, https://doi.org/10.5194/egusphere-2023-1460, 2023
Short summary
Short summary
Europe is regularly affected by compound events and natural hazards that occur simultaneously or with a temporal lag and are connected with disproportional impacts. Within the interdisciplinary project climXtreme (https://climxtreme.net/) we investigate the interplay of these events, their characteristics and changes, intensity, frequency and uncertainties in the past, present and future, as well as the associated impacts on different socio-economic sectors in Germany and Central Europe.
Zbigniew P. Piotrowski, Jaro Hokkanen, Daniel Caviedes-Voullieme, Olaf Stein, and Stefan Kollet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1079, https://doi.org/10.5194/egusphere-2023-1079, 2023
Preprint withdrawn
Short summary
Short summary
The computer programs capable of simulation of Earth system components evolve, adapting new fundamental science concepts and more observational data on more and more powerful computer hardware. Adaptation of a large scientific program to a new type of hardware is costly. In this work we propose cheap and simple but effective strategy that enable computation using graphic processing units, based on automated program code modification. This results in better resolution and/or longer predictions.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Joni-Pekka Pietikaeinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
EGUsphere, https://doi.org/10.5194/egusphere-2023-1463, https://doi.org/10.5194/egusphere-2023-1463, 2023
Short summary
Short summary
With a team of 20 authors from different countries, we tried to compile the impacts of drought and heat on European forests in the period 2018–2022. This is a research approach that transcends subject and country borders.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Mohamed Saadi, Carina Furusho-Percot, Alexandre Belleflamme, Ju-Yu Chen, Silke Trömel, and Stefan Kollet
Nat. Hazards Earth Syst. Sci., 23, 159–177, https://doi.org/10.5194/nhess-23-159-2023, https://doi.org/10.5194/nhess-23-159-2023, 2023
Short summary
Short summary
On 14 July 2021, heavy rainfall fell over central Europe, causing considerable damage and human fatalities. We analyzed how accurate our estimates of rainfall and peak flow were for these flooding events in western Germany. We found that the rainfall estimates from radar measurements were improved by including polarimetric variables and their vertical gradients. Peak flow estimates were highly uncertain due to uncertainties in hydrological model parameters and rainfall measurements.
Liubov Poshyvailo-Strube, Rolf Müller, Stephan Fueglistaler, Michaela I. Hegglin, Johannes C. Laube, C. Michael Volk, and Felix Ploeger
Atmos. Chem. Phys., 22, 9895–9914, https://doi.org/10.5194/acp-22-9895-2022, https://doi.org/10.5194/acp-22-9895-2022, 2022
Short summary
Short summary
Brewer–Dobson circulation (BDC) controls the composition of the stratosphere, which in turn affects radiation and climate. As the BDC cannot be measured directly, it is necessary to infer its strength and trends indirectly. In this study, we test in the
model worlddifferent methods for estimating the mean age of air trends based on a combination of stratospheric water vapour and methane data. We also provide simple practical advice of a more reliable estimation of the mean age of air trends.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Yueling Ma, Carsten Montzka, Bagher Bayat, and Stefan Kollet
Hydrol. Earth Syst. Sci., 25, 3555–3575, https://doi.org/10.5194/hess-25-3555-2021, https://doi.org/10.5194/hess-25-3555-2021, 2021
Short summary
Short summary
This study utilized spatiotemporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) data from integrated hydrologic simulation results over Europe in combination with Long Short-Term Memory (LSTM) networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.
Susannah Rennie, Klaus Goergen, Christoph Wohner, Sander Apweiler, Johannes Peterseil, and John Watkins
Earth Syst. Sci. Data, 13, 631–644, https://doi.org/10.5194/essd-13-631-2021, https://doi.org/10.5194/essd-13-631-2021, 2021
Short summary
Short summary
This paper describes a pan-European climate service data product intended for ecological researchers. Access to regional climate scenario data will save ecologists time, and, for many, it will allow them to work with data resources that they will not previously have used due to a lack of knowledge and skills to access them. Providing easy access to climate scenario data in this way enhances long-term ecological research, for example in general regional climate change or impact assessments.
Timo Keber, Harald Bönisch, Carl Hartick, Marius Hauck, Fides Lefrancois, Florian Obersteiner, Akima Ringsdorf, Nils Schohl, Tanja Schuck, Ryan Hossaini, Phoebe Graf, Patrick Jöckel, and Andreas Engel
Atmos. Chem. Phys., 20, 4105–4132, https://doi.org/10.5194/acp-20-4105-2020, https://doi.org/10.5194/acp-20-4105-2020, 2020
Short summary
Short summary
In this paper we summarize observations of short-lived halocarbons in the tropopause region. We show that, especially during winter, the levels of short-lived bromine gases at the extratropical tropopause are higher than at the tropical tropopause. We discuss the impact of the distributions on stratospheric bromine levels and compare our observations to two models with four different emission scenarios.
Benjamin N. O. Kuffour, Nicholas B. Engdahl, Carol S. Woodward, Laura E. Condon, Stefan Kollet, and Reed M. Maxwell
Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, https://doi.org/10.5194/gmd-13-1373-2020, 2020
Short summary
Short summary
Integrated hydrologic models (IHMs) were developed in order to allow for more accurate simulations of real-world ecohydrologic conditions. Many IHMs exist, and the literature can be dense, so it is often difficult to understand what a specific model can and cannot do. We provide a review of the current core capabilities, solution techniques, communication structure with other models, some limitations, and potential future improvements of one such open-source integrated model called ParFlow.
Bibi S. Naz, Wolfgang Kurtz, Carsten Montzka, Wendy Sharples, Klaus Goergen, Jessica Keune, Huilin Gao, Anne Springer, Harrie-Jan Hendricks Franssen, and Stefan Kollet
Hydrol. Earth Syst. Sci., 23, 277–301, https://doi.org/10.5194/hess-23-277-2019, https://doi.org/10.5194/hess-23-277-2019, 2019
Short summary
Short summary
This study investigates the value of assimilating coarse-resolution remotely sensed soil moisture data into high-resolution land surface models for improving soil moisture and runoff modeling. The soil moisture estimates in this study, with complete spatio-temporal coverage and improved spatial resolution from the assimilation, offer a new reanalysis product for the monitoring of surface soil water content and other hydrological fluxes at 3 km resolution over Europe.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Liubov Poshyvailo, Rolf Müller, Paul Konopka, Gebhard Günther, Martin Riese, Aurélien Podglajen, and Felix Ploeger
Atmos. Chem. Phys., 18, 8505–8527, https://doi.org/10.5194/acp-18-8505-2018, https://doi.org/10.5194/acp-18-8505-2018, 2018
Short summary
Short summary
Water vapour (H2O) in the UTLS is a key player for global radiation, which is critical for predictions of future climate change. We investigate the effects of current uncertainties in tropopause temperature, horizontal transport and small-scale mixing on simulated H2O, using the Chemical Lagrangian Model of the Stratosphere. Our sensitivity studies provide new insights into the leading processes controlling stratospheric H2O, important for assessing and improving climate model projections.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
Short summary
Short summary
In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Stefan J. Kollet
Hydrol. Earth Syst. Sci., 20, 2801–2809, https://doi.org/10.5194/hess-20-2801-2016, https://doi.org/10.5194/hess-20-2801-2016, 2016
Wolfgang Kurtz, Guowei He, Stefan J. Kollet, Reed M. Maxwell, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, https://doi.org/10.5194/gmd-9-1341-2016, 2016
Short summary
Short summary
This paper describes the development of a modular data assimilation (DA) system for the integrated Earth system model TerrSysMP with the help of the PDAF data assimilation library.
Currently, pressure and soil moisture data can be used to update model states and parameters in the subsurface compartment of TerrSysMP.
Results from an idealized twin experiment show that the developed DA system provides a good parallel performance and is also applicable for high-resolution modelling problems.
P. Shrestha, M. Sulis, C. Simmer, and S. Kollet
Hydrol. Earth Syst. Sci., 19, 4317–4326, https://doi.org/10.5194/hess-19-4317-2015, https://doi.org/10.5194/hess-19-4317-2015, 2015
Short summary
Short summary
This study highlights the grid resolution dependence of energy and water balance of the 3-D physically based integrated surface-groundwater model. The non-local controls of soil moisture were found to be highly grid resolution dependent, but the local vegetation control strongly modulates the scaling behavior of surface energy fluxes. For coupled runs, variability in patterns of surface fluxes due to this scale dependence can affect the simulated atmospheric boundary layer and local circulation.
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
R. M. Maxwell, L. E. Condon, and S. J. Kollet
Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, https://doi.org/10.5194/gmd-8-923-2015, 2015
Short summary
Short summary
A model that simulates groundwater and surface water flow has been developed for the major river basins of the continental United States. Fundamental data sets provide input to the model resulting in a natural organization of stream networks and groundwater flow that is compared to observations of surface water and groundwater. Model results show relationships between flow and area that are moderated by aridity and represent an important step toward integrated hydrological prediction.
E. Katragkou, M. García-Díez, R. Vautard, S. Sobolowski, P. Zanis, G. Alexandri, R. M. Cardoso, A. Colette, J. Fernandez, A. Gobiet, K. Goergen, T. Karacostas, S. Knist, S. Mayer, P. M. M. Soares, I. Pytharoulis, I. Tegoulias, A. Tsikerdekis, and D. Jacob
Geosci. Model Dev., 8, 603–618, https://doi.org/10.5194/gmd-8-603-2015, https://doi.org/10.5194/gmd-8-603-2015, 2015
F. Gasper, K. Goergen, P. Shrestha, M. Sulis, J. Rihani, M. Geimer, and S. Kollet
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, https://doi.org/10.5194/gmd-7-2531-2014, 2014
S. Kotlarski, K. Keuler, O. B. Christensen, A. Colette, M. Déqué, A. Gobiet, K. Goergen, D. Jacob, D. Lüthi, E. van Meijgaard, G. Nikulin, C. Schär, C. Teichmann, R. Vautard, K. Warrach-Sagi, and V. Wulfmeyer
Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, https://doi.org/10.5194/gmd-7-1297-2014, 2014
Related subject area
Topics: Atmosphere | Interactions: Land/atmosphere interactions | Methods: Earth system and climate modeling
Scaling artificial heat islands to enhance precipitation in the United Arab Emirates
Oliver Branch, Lisa Jach, Thomas Schwitalla, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 15, 109–129, https://doi.org/10.5194/esd-15-109-2024, https://doi.org/10.5194/esd-15-109-2024, 2024
Short summary
Short summary
In the United Arab Emirates, water scarcity is reaching a crisis point, and new methods for obtaining freshwater are urgently needed. Regional climate engineering with large artificial heat islands can enhance desert precipitation by increasing cloud development. Through model simulation, we show that heat islands of 20 × 20 km or larger can potentially produce enough annual rainfall to supply thousands of people. Thus, artificial heat islands should be made a high priority for further research.
Cited articles
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J., Aguilar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, https://doi.org/10.1029/2005JD006290, 2006. a
Amengual, A., Homar, V., Romero, R., Brooks, H., Ramis, C., Gordaliza, M., and Alonso, S.: Projections of heat waves with high impact on human health in Europe, Glob. Planet. Change, 119, 71–84, https://doi.org/10.1016/j.gloplacha.2014.05.006, 2014. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barlage, M., Tewari, M., Chen, F., Miguez-Macho, G., Yang, Z.-L., and Niu, G.-Y.: The effect of groundwater interaction in North American regional climate simulations with WRF/Noah-MP, Climatic Change, 129, 485–498, https://doi.org/10.1007/s10584-014-1308-8, 2015. a
Barlage, M., Chen, F., Rasmussen, R., Zhang, Z., and Miguez-Macho, G.: The Importance of Scale-Dependent Groundwater Processes in Land-Atmosphere Interactions Over the Central United States, Geophys. Res. Lett., 48, e2020GL092171, https://doi.org/10.1029/2020GL092171, 2021. a, b
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science, 332, 220–224, https://doi.org/10.1126/science.1201224, 2011. a
Barriopedro, D., García-Herrera, R., Ordóñez, C., Miralles, D. G., and Salcedo-Sanz, S.: Heat Waves: Physical Understanding and Scientific Challenges, Rev. Geophys., 61, e2022RG000780, https://doi.org/10.1029/2022RG000780, 2023. a
Bellprat, O., Kotlarski, S., Lüthi, D., Elía, R. D., Frigon, A., Laprise, R., and Schär, C.: Objective Calibration of Regional Climate Models: Application over Europe and North America, J. Clim., 29, 819–838, https://doi.org/10.1175/JCLI-D-15-0302.1, 2016. a
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a
Christensen, J. H. and Christensen, O. B.: A summary of the PRUDENCE model projections of changes in European climate by the end of this century, Climatic Change, 81, 7–30, https://doi.org/10.1007/s10584-006-9210-7, 2007. a
Christensen, O. B. and Kjellström, E.: Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections, Clim. Dynam., 54, 4293–4308, https://doi.org/10.1007/s00382-020-05229-y, 2020. a
Christidis, N., Jones, G., and Stott, P.: Dramatically increasing chance of extremely hot summers since the 2003 European heatwave, Nat. Climatic Change, 5, 46–50, https://doi.org/10.1038/nclimate2468, 2015. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018. a
Daac, L.: Global 30 arc-second elevation data set GTOPO30, Land process distributed active archive center, https://www.usgs.gov/centers/eros/science/ (last access: 15 December 2022), 2004. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kallberg, P., Koehler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Déqué, M., Rowell, D. P., Lüthi, D., Giorgi, F., Christensen, J. H., Rockel, B., Jacob, D., Kjellström, E., de Castro, M., and van den Hurk, B.: An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections, Climatic Change, 81, 53–70, https://doi.org/10.1007/s10584-006-9228-x, 2007. a
Déqué, M., Somot, S., Sanchez-Gomez, E., Goodess, C. M., Jacob, D., Lenderink, G., and Christensen, O. B.: The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability, Climatic Change, 38, 951–964, https://doi.org/10.1007/s00382-011-1053-x, 2012. a
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 Advances, 2, e2020AV000283, https://doi.org/10.1029/2020AV000283, 2021. a, b, c
Dufresne, J. -L., Foujols, M. -A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J. -P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J. -Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M. -P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1, 2013. a
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Phillipps, P. J., Sentman, L. T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., and Zadeh, N.: GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics, J. Clim., 25, 6646–6665, https://doi.org/10.1175/JCLI-D-11-00560.1, 2012. a
Duscher, K., Günther, A., Richts, A., Clos, P., Philipp, U., and Struckmeier, W.: The GIS layers of the “International Hydrogeological Map of Europe 1:1 500 000” in a vector format, Hydrogeol. J., 23, 1867–1875, https://doi.org/10.1007/s10040-015-1296-4, 2015. a
Erdenebat, E. and Tomonori, S.: Role of soil moisture-atmosphere feedback during high temperature events in 2002 over Northeast Eurasia, Prog. Earth. Planet. Sci., 5, 37, https://doi.org/10.1186/s40645-018-0195-4, 2018. a
Evin, G., Somot, S., and Hingray, B.: Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble, Earth Syst. Dynam., 12, 1543–1569, https://doi.org/10.5194/esd-12-1543-2021, 2021. a, b
FAO: FAO/UNESCO Soil Map of the World, Revised Legend, with corrections and updates, World Soil Resources Report 60, FAO, Rome, https://www.fao.org/3/bl892e/bl892e.pdf (last access: 1 December 2022), 1988. a
Fernández, J., Frías, M. D., Cabos, W. D., Cofiño, A. S., Domínguez, M., Fita, L., Gaertner, M. A., García-Díez, M., Gutiérrez, J. M., Jiménez-Guerrero, P., Liguori, G., Montávez, J. P., Romera, R., and Sánchez, E.: Consistency of climate change projections from multiple global and regional model intercomparison projects, Clim. Dynam., 52, 1139–1156, https://doi.org/10.1007/s00382-018-4181-8, 2019. a
Fischer, E. M. and Schär, C.: Consistent geographical patterns of changes in high-impact European heatwaves, Nat. Geosci., 3, 398–403, https://doi.org/10.1038/ngeo866, 2010. a
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. a, b, c
Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Klein Tank, A. M. G., and Peterson, T.: Observed coherent changes in climatic extremes during the second half of the twentieth century, Clim. Res., 19, 193–212, https://doi.org/10.3354/cr019193, 2002. a, b
Friedl, M., McIver, D., Hodges, J., Zhang, X., Muchoney, D., Strahler, A., Woodcock, C., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287–302, https://doi.org/10.1016/S0034-4257(02)00078-0, 2002. a
Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J., and Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation, Sci. Data, 6, 320, https://doi.org/10.1038/s41597-019-0328-7, 2019. a, b, c, d
Gasper, F., Goergen, K., Shrestha, P., Sulis, M., Rihani, J., Geimer, M., and Kollet, S.: Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q), Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, 2014. a, b
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Syst., 5, 572–597, https://doi.org/10.1002/jame.20038, 2013. a, b, c
Giorgi, F. and Gutowski, W. J.: Regional Dynamical Downscaling and the CORDEX Initiative, Annu. Rev. Env. Resour., 40, 467–490, https://doi.org/10.1146/annurev-environ-102014-021217, 2015. a
Gleeson, T., Moosdorf, N., Hartmann, J., and van Beek, L. P. H.: A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity, Geophys. Res. Lett., 41, 3891–3898, https://doi.org/10.1002/2014GL059856, 2014. a
Grasselt, R., Schüttemeyer, D., Warrach-Sagi, K., Ament, F., and Simmer, C.: Validation of TERRA-ML with discharge measurements, Meteorol. Z., 17, 763–773, https://doi.org/10.1127/0941-2948/2008/0334, 2008. a
Gutowski, W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016. a, b
Haghighi, E., Short Gianotti, D. J., Akbar, R., Salvucci, G. D., and Entekhabi, D.: Soil and Atmospheric Controls on the Land Surface Energy Balance: A Generalized Framework for Distinguishing Moisture-Limited and Energy-Limited Evaporation Regimes, Water Resour. Res., 54, 1831–1851, https://doi.org/10.1002/2017WR021729, 2018. a
Hari, V., Rakovec, O., Markonis, Y., Hanel, M., and Kumar, R.: Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming, Sci. Rep., 10, 12207, https://doi.org/10.1038/s41598-020-68872-9, 2020. a
Hartick, C., Furusho-Percot, C., Goergen, K., and Kollet, S.: An Interannual Probabilistic Assessment of Subsurface Water Storage Over Europe Using a Fully Coupled Terrestrial Model, Water Resour. Res., 57, e2020WR027828, https://doi.org/10.1029/2020WR027828, 2021. a, b, c, d
Hartick, C., Furusho-Percot, C., Clark, M. P., and Kollet, S.: An Interannual Drought Feedback Loop Affects the Surface Energy Balance and Cloud Properties, Geophys. Res. Lett., 49, e2022GL100924, https://doi.org/10.1029/2022GL100924, 2022. a
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional Climate Predictions, Bull. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E., and Raymond, C.: Review of Recent Advances in Research on Extreme Heat Events, Curr. Clim. Chang. Rep., 2, 242–259, https://doi.org/10.1007/s40641-016-0042-x, 2016. a, b
Iles, C. E., Vautard, R., Strachan, J., Joussaume, S., Eggen, B. R., and Hewitt, C. D.: The benefits of increasing resolution in global and regional climate simulations for European climate extremes, Geosci. Model Dev., 13, 5583–5607, https://doi.org/10.5194/gmd-13-5583-2020, 2020. a
Jach, L., Schwitalla, T., Branch, O., Warrach-Sagi, K., and Wulfmeyer, V.: Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe, Earth Syst. Dynam., 13, 109–132, https://doi.org/10.5194/esd-13-109-2022, 2022. a
Jacob, D. and Podzun, R.: Sensitivity studies with the regional climate model REMO, Meteor. Atmos. Phys., 63, 119–129, https://doi.org/10.1007/BF01025368, 1997. a
Jacob, D., Teichmann, C., Sobolowski, S., and et al.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a, b
Jiang, N., Zhu, C., Hu, Z.-Z., McPhaden, M. J., Chen, D., Liu, B., Ma, S., Yan, Y., Zhou, T., Qian, W., Luo, J., Yang, X., Liu, F., and Zhu, Y.: Enhanced risk of record-breaking regional temperatures during the 2023–24 El Niño, Sci. Rep., 14, 2521, https://doi.org/10.1038/s41598-024-52846-2, 2024. a
Kautz, L.-A., Martius, O., Pfahl, S., Pinto, J. G., Ramos, A. M., Sousa, P. M., and Woollings, T.: Atmospheric blocking and weather extremes over the Euro-Atlantic sector – a review, Weather Clim. Dynam., 3, 305–336, https://doi.org/10.5194/wcd-3-305-2022, 2022. a
Keune, J., Gasper, F., Goergen, K., Hense, A., Shrestha, P., Sulis, M., and Kollet, S.: Studying the influence of groundwater representations on land surface-atmosphere feedbacks during the European heat wave in 2003, J. Geophys. Res.-Atmos., 121, 13301–13325, https://doi.org/10.1002/2016JD025426, 2016. a, b, c, d
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water. Resour., 29, 945–958, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model, Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007WR006004, 2008. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.: Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H.-Y., Wiese, D. N., and Yuan, D.-N.: Extending the Global Mass Change Data Record: GRACE Follow-On Instrument and Science Data Performance, Geophys. Res. Lett., 47, e2020GL088 306, https://doi.org/10.1029/2020GL088306, 2020. a
Lemus-Canovas, M., Insua-Costa, D., Trigo, R. M., and Miralles, D. G.: Record-shattering 2023 Spring heatwave in western Mediterranean amplified by long-term drought, NPJ Clim. Atmos. Sci., 7, 25, https://doi.org/10.1038/s41612-024-00569-6, 2024. a
Lhotka, O. and Kyselý, J.: Characterizing joint effects of spatial extent, temperature magnitude and duration of heat waves and cold spells over Central Europe, Int. J. Climatol., 35, 1232–1244, https://doi.org/10.1002/joc.4050, 2015. a
Lhotka, O., Kyselý, J., and Plavcová, E.: Evaluation of major heat waves’ mechanisms in EURO-CORDEX RCMs over Central Europe, Clim. Dynam., 50, 4249–4262, https://doi.org/10.1007/s00382-017-3873-9, 2018. a
Liang, X., Xie, Z., and Huang, M.: A new parameterization for surface and groundwater interactions and its impact on water budgets with the variable infiltration capacity (VIC) land surface model, J. Geophys. Res., 108, 8613, https://doi.org/10.1029/2002JD003090, 2003. a
Liu, X., He, B., Guo, L., Huang, L., and Chen, D.: Similarities and Differences in the Mechanisms Causing the European Summer Heatwaves in 2003, 2010, and 2018, Earth's Future, 8, e2019EF001386, https://doi.org/10.1029/2019EF001386, 2020. a
Ma, Y., Montzka, C., Naz, B. S., and Kollet, S.: Advancing AI-based pan-European groundwater monitoring, Environ. Res. Lett., 17, 114037, https://doi.org/10.1088/1748-9326/ac9c1e, 2022. a
Martínez-de la Torre, A. and Miguez-Macho, G.: Groundwater influence on soil moisture memory and land–atmosphere fluxes in the Iberian Peninsula, Hydrol. Earth Syst. Sci., 23, 4909–4932, https://doi.org/10.5194/hess-23-4909-2019, 2019. a, b
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N.and Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, I., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and B., Z. (Eds.): IPCC report, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FullReport.pdf (last access: 1 December 2022), 2021. a
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016. a
Maxwell, R. M. and Miller, N. L.: Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 6, 233–247, https://doi.org/10.1175/JHM422.1, 2005. a
Maxwell, R. M., Chow, F. K., and Kollet, S. J.: The groundwater–land-surface–atmosphere connection: Soil moisture effects on the atmospheric boundary layer in fully-coupled simulations, Adv. Water Resour., 30, 2447–2466, https://doi.org/10.1016/j.advwatres.2007.05.018, 2007. a
Mearns, L. O., Lettenmaier, D. P., and McGinnis, S.: Uses of Results of Regional Climate Model Experiments for Impacts and Adaptation Studies: the Example of NARCCAP, Curr. Clim. Change Rep., 1, 1–9, https://doi.org/10.1007/s40641-015-0004-8, 2015. a
Molina, M. O., Sánchez, E., and Gutiérrez, C.: Future heat waves over the Mediterranean from an Euro-CORDEX regional climate model ensemble, Sci. Rep., 10, 8801, https://doi.org/10.1038/s41598-020-65663-0, 2020. a
Mu, M., Pitman, A. J., De Kauwe, M. G., Ukkola, A. M., and Ge, J.: How do groundwater dynamics influence heatwaves in southeast Australia?, Weather Clim. Extrem., 37, 100479, https://doi.org/10.1016/j.wace.2022.100479, 2022. a
Nairn, J. R. and Fawcett, R. J. B.: The excess heat factor: a metric for heatwave intensity and its use in classifying heatwave severity, Int. J. Environ. Res. Publ. Health, 12, 227–253, https://doi.org/10.3390/ijerph120100227, 2014. a
Naz, B. S., Sharples, W., Ma, Y., Goergen, K., and Kollet, S.: Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe, Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, 2023. a
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data, J. Geophys. Res.-Atmos., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007. a
Oleson, K., Dai, Y., Bonan, G. B., Bosilovichm, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G.-Y., Thornton, P., Vertenstein, M., Yang, Z.-L., and Zeng, X.: Technical Description of the Community Land Model (CLM) (No. NCAR/TN-461+STR), Tech. Rep., University Corporation for Atmospheric Research, https://doi.org/10.5065/D6N877R0, 2004. a
Oleson, K. W., Niu, G.-Y., Yang, Z.-L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S., Dai, A., and Qian, T.: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res.-Biogeo., 113, G01021, https://doi.org/10.1029/2007JG000563, 2008. a, b
Pal, J. S. and Eltahir, E. A. B.: Pathways Relating Soil Moisture Conditions to Future Summer Rainfall within a Model of the Land–Atmosphere System, J. Clim., 14, 1227–1242, https://doi.org/10.1175/1520-0442(2001)014<1227:PRSMCT>2.0.CO;2, 2001. a
Perkins, S. E. and Alexander, L. V.: On the Measurement of Heat Waves, J. Clim., 26, 4500–4517, https://doi.org/10.1175/JCLI-D-12-00383.1, 2013. a
Plavcová, E. and Kyselý, J.: Overly persistent circulation in climate models contributes to overestimated frequency and duration of heat waves and cold spells, Clim. Dynam., 46, 2805–2820, https://doi.org/10.1007/s00382-015-2733-8, 2016. a
Poshyvailo-Strube, L., Wagner, N., Goergen, K., Furusho-Percot, C., Hartick, C., and Kollet, S.: Regional climate scenarios with the coupled TSMP in the context of HI-CAM and the WCRP EURO-CORDEX initiative, Jülich DATA, V1 [data set], https://doi.org/10.26165/JUELICH-DATA/9S3V5K, 2023. a, b
Pothapakula, P. K., Primo, C., Sørland, S., and Ahrens, B.: The synergistic impact of ENSO and IOD on Indian summer monsoon rainfall in observations and climate simulations – an information theory perspective, Earth Syst. Dynam., 11, 903–923, https://doi.org/10.5194/esd-11-903-2020, 2020. a
Prein, A. F., Gobiet, A., Truhetz, H., Keuler, K., Goergen, K., Teichmann, C., Fox Maule, C., van Meijgaard, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., and Jacob, D.: Precipitation in the EURO-CORDEX 0.11° and 0.44° simulations: high resolution, high benefits?, Clim. Dynam., 46, 383–412, https://doi.org/10.1007/s00382-015-2589-y, 2016. a
Rockel, B., Will, A., and Hense, A.: The Regional Climate Model COSMO-CLM (CCLM), Meteorol. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309, 2008. a, b
Rummukainen, M.: Added value in regional climate modeling, WIREs Climatic Change, 7, 145–159, https://doi.org/10.1002/wcc.378, 2016. a
Russo, S., Sillmann, J., and Fischer, E. M.: Top ten European heatwaves since 1950 and their occurrence in the coming decades, Environ. Res. Lett., 10, 124003, https://doi.org/10.1088/1748-9326/10/12/124003, 2015. a
Schlemmer, L., Schär, C., Lüthi, D., and Strebel, L.: A Groundwater and Runoff Formulation for Weather and Climate Models, J. Adv. Model. Earth Syst., 10, 1809–1832, https://doi.org/10.1029/2017MS001260, 2018. a, b
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. a
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. a, b
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/MWR-D-14-00029.1, 2014. a, b, c
Song, Y. M., Wang, Z. F., Qi, L. L., and Huang, A. N.: Soil Moisture Memory and Its Effect on the Surface Water and Heat Fluxes on Seasonal and Interannual Time Scales, J. Geophys. Res.-Atmos., 124, 10730–10741, https://doi.org/10.1029/2019JD030893, 2019. a
Sørland, S. L., Schär, C., Lüthi, D., and Kjellström, E.: Bias patterns and climate change signals in GCM-RCM model chains, Environ. Res. Lett., 13, 074017, https://doi.org/10.1088/1748-9326/aacc77, 2018. a
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. a
Stott, P. A., Stone, D. A., and Allen, M. R.: Human contribution to the European heatwave of 2003, Nature, 432, 610–614, https://doi.org/10.1038/nature03089, 2004. a
Sulikowska, A. and Wypych, A.: Summer temperature extremes in Europe: how does the definition affect the results?, Theor. Appl. Climatol., 141, 19–30, https://doi.org/10.1007/s00704-020-03166-8, 2020. a, b
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, Bull. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b
Teuling, A. J., Uijlenhoet, R., van den Hurk, B., and Seneviratne, S. I.: Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters, J. Hydrometeorol., 10, 751–765, https://doi.org/10.1175/2008JHM1033.1, 2009. a
Tomczyk, A. M. and Bednorz, E.: Heat waves in Central Europe and their circulation conditions, Int. J. Climatol., 36, 770–782, https://doi.org/10.1002/joc.4381, 2016. a
Torma, C., Giorgi, F., and Coppola, E.: Added value of regional climate modeling over areas characterized by complex terrain – Precipitation over the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972, https://doi.org/10.1002/2014JD022781, 2015. a
Turco, M., Sanna, A., Herrera, S., Llasat, M.-C., and Gutiérrez, J. M.: Large biases and inconsistent climate change signals in ENSEMBLES regional projections, Climatic Change, 120, 859–869, https://doi.org/10.1007/s10584-013-0844-y, 2013. a
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013. a
Vautard, R., Gobiet, A., Jacob, D., Belda, M., Colette, A., Déqué, M., Fernández, J., García-Díez, M., Goergen, K., Güttler, I., Halenka, T., Karacostas, T., Katragkou, E., Keuler, K., Kotlarski, S., Mayer, S., van Meijgaard, E., Nikulin, G., Patarčić, M., Scinocca, J., Sobolowski, S., Suklitsch, M., Teichmann, C., Warrach-Sagi, K., Wulfmeyer, V., and Yiou, P.: The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project, Clim. Dynam., 41, 2555–2575, https://doi.org/10.1007/s00382-013-1714-z, 2013. a, b, c, d
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. a
Vogt, J., Soille, P., De Jager, A., Rimaviciute, E., Mehl, W., Foisneau, S., Bodis, K., Dusart, J., Paracchini, M., Haastrup, P., and Bamps, C.: A pan-European River and Catchment Database, JRC Reference Report, Joint Research Centre, Institute for Environment and Sustainability, Publications Office, https://doi.org/10.2788/35907, 2007. a
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013. a
Yang, L., Sun, G., Zhi, L., and Zhao, J.: Negative soil moisture-precipitation feedback in dry and wet regions, Sci. Rep., 8, 4026, https://doi.org/10.1038/s41598-018-22394-7, 2018. a
Yin, C., Yang, Y., Chen, X., Yue, X., Liu, Y., and Xin, Y.: Changes in global heat waves and its socioeconomic exposure in a warmer future, Clim. Risk Manag., 38, 100459, https://doi.org/10.1016/j.crm.2022.100459, 2022. a
Yule, E. L., Hegerl, G., Schurer, A., and Hawkins, E.: Using early extremes to place the 2022 UK heat waves into historical context, Atmos. Sci. Lett., 24, e1159, https://doi.org/10.1002/asl.1159, 2023. a
Zhang, R., Sun, C., Zhu, J., Zhang, R., and Li, W.: Increased European heat waves in recent decades in response to shrinking Arctic sea ice and Eurasian snow cover, npj Clim. Atmos. Sci., 3, 7, https://doi.org/10.1038/s41612-020-0110-8, 2020. a
Zhang, X., Hegerl, G., Zwiers, F., and Kenyon, J.: Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes, J. Clim., 38, 1641–1651, https://doi.org/10.1175/JCLI3366.1, 2005. a
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Climatic Change, 2, 851–870, https://doi.org/10.1002/wcc.147, 2011. a
Short summary
Groundwater (GW) representation is simplified in most regional climate models. Here, we introduce a unique Terrestrial Systems Modeling Platform (TSMP) dataset with an explicit representation of GW, in the context of dynamical downscaling of GCMs for climate change studies. We compare the heat events statistics of TSMP and the CORDEX ensemble. Our results show that TSMP systematically simulates fewer heat waves, and they are shorter and less intense.
Groundwater (GW) representation is simplified in most regional climate models. Here, we...
Altmetrics
Final-revised paper
Preprint