Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4569-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4569-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Verena Bessenbacher
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
Sonia Isabelle Seneviratne
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
Lukas Gudmundsson
Institute for Atmospheric and Climate Science, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
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Dominik L. Schumacher, Mariam Zachariah, Friederike Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Maja Vahlberg, Roop Singh, Dorothy Heinrich, Julie Arrighi, Maarten van Aalst, Mathias Hauser, Martin Hirschi, Verena Bessenbacher, Lukas Gudmundsson, Hiroko K. Beaudoing, Matthew Rodell, Sihan Li, Wenchang Yang, Gabriel A. Vecchi, Luke J. Harrington, Flavio Lehner, Gianpaolo Balsamo, and Sonia I. Seneviratne
Earth Syst. Dynam., 15, 131–154, https://doi.org/10.5194/esd-15-131-2024, https://doi.org/10.5194/esd-15-131-2024, 2024
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The 2022 summer was accompanied by widespread soil moisture deficits, including an unprecedented drought in Europe. Combining several observation-based estimates and models, we find that such an event has become at least 5 and 20 times more likely due to human-induced climate change in western Europe and the northern extratropics, respectively. Strong regional warming fuels soil desiccation; hence, projections indicate even more potent future droughts as we progress towards a 2 °C warmer world.
Ulrike Proske, Verena Bessenbacher, Zane Dedekind, Ulrike Lohmann, and David Neubauer
Atmos. Chem. Phys., 21, 5195–5216, https://doi.org/10.5194/acp-21-5195-2021, https://doi.org/10.5194/acp-21-5195-2021, 2021
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Ice crystals falling out of one cloud can initiate freezing in a second cloud below. We estimate the occurrence frequency of this natural cloud seeding over Switzerland from satellite data and sublimation calculations. We find that such situations with an ice cloud above another cloud are frequent and that the falling crystals survive the fall between two clouds in a significant number of cases, suggesting that natural cloud seeding is an important phenomenon over Switzerland.
Hao Pan, Lukas Gudmundsson, Mathias Hauser, Jonas Schwaab, Yann Quilcaille, and Sonia I. Seneviratne
EGUsphere, https://doi.org/10.5194/egusphere-2025-3052, https://doi.org/10.5194/egusphere-2025-3052, 2025
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Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
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Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
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This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
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Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
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We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
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Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
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Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, https://doi.org/10.5194/nhess-24-1975-2024, 2024
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Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Steven J. De Hertog, Carmen E. Lopez-Fabara, Ruud van der Ent, Jessica Keune, Diego G. Miralles, Raphael Portmann, Sebastian Schemm, Felix Havermann, Suqi Guo, Fei Luo, Iris Manola, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 15, 265–291, https://doi.org/10.5194/esd-15-265-2024, https://doi.org/10.5194/esd-15-265-2024, 2024
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Changes in land use are crucial to achieve lower global warming. However, despite their importance, the effects of these changes on moisture fluxes are poorly understood. We analyse land cover and management scenarios in three climate models involving cropland expansion, afforestation, and irrigation. Results show largely consistent influences on moisture fluxes, with cropland expansion causing a drying and reduced local moisture recycling, while afforestation and irrigation show the opposite.
Dominik L. Schumacher, Mariam Zachariah, Friederike Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Maja Vahlberg, Roop Singh, Dorothy Heinrich, Julie Arrighi, Maarten van Aalst, Mathias Hauser, Martin Hirschi, Verena Bessenbacher, Lukas Gudmundsson, Hiroko K. Beaudoing, Matthew Rodell, Sihan Li, Wenchang Yang, Gabriel A. Vecchi, Luke J. Harrington, Flavio Lehner, Gianpaolo Balsamo, and Sonia I. Seneviratne
Earth Syst. Dynam., 15, 131–154, https://doi.org/10.5194/esd-15-131-2024, https://doi.org/10.5194/esd-15-131-2024, 2024
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The 2022 summer was accompanied by widespread soil moisture deficits, including an unprecedented drought in Europe. Combining several observation-based estimates and models, we find that such an event has become at least 5 and 20 times more likely due to human-induced climate change in western Europe and the northern extratropics, respectively. Strong regional warming fuels soil desiccation; hence, projections indicate even more potent future droughts as we progress towards a 2 °C warmer world.
Yann Quilcaille, Lukas Gudmundsson, and Sonia I. Seneviratne
Earth Syst. Dynam., 14, 1333–1362, https://doi.org/10.5194/esd-14-1333-2023, https://doi.org/10.5194/esd-14-1333-2023, 2023
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Climate models are powerful tools, but they have high computational costs, hindering their use in exploring future climate extremes. We demonstrate MESMER-X, the only existing emulator for spatial climate extremes (heatwaves, fires, droughts) that mimics all of their relevant properties. Thanks to its negligible computational cost, MESMER-X may greatly accelerate the exploration of future climate extremes or enable the integration of climate extremes in economic and financial models.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Steven J. De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard L. Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 14, 629–667, https://doi.org/10.5194/esd-14-629-2023, https://doi.org/10.5194/esd-14-629-2023, 2023
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Land cover and land management changes are important strategies for future land-based mitigation. We investigate the climate effects of cropland expansion, afforestation, irrigation and wood harvesting using three Earth system models. Results show that these have important implications for surface temperature where the land cover and/or management change occur and in remote areas. Idealized afforestation causes global warming, which might offset the cooling effect from enhanced carbon uptake.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Steven J. De Hertog, Carmen E. Lopez-Fabara, Ruud van der Ent, Jessica Keune, Diego G. Miralles, Raphael Portmann, Sebastian Schemm, Felix Havermann, Suqi Guo, Fei Luo, Iris Manola, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2023-953, https://doi.org/10.5194/egusphere-2023-953, 2023
Preprint archived
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Land cover and management changes can affect the climate and water availability. In this study we use climate model simulations of extreme global land cover changes (afforestation, deforestation) and land management changes (irrigation) to understand the effects on the global water cycle and local to continental water availability. We show that cropland expansion generally leads to higher evaporation and lower amounts of precipitation and afforestation and irrigation expansion to the opposite.
Francisco José Cuesta-Valero, Hugo Beltrami, Almudena García-García, Gerhard Krinner, Moritz Langer, Andrew H. MacDougall, Jan Nitzbon, Jian Peng, Karina von Schuckmann, Sonia I. Seneviratne, Wim Thiery, Inne Vanderkelen, and Tonghua Wu
Earth Syst. Dynam., 14, 609–627, https://doi.org/10.5194/esd-14-609-2023, https://doi.org/10.5194/esd-14-609-2023, 2023
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Climate change is caused by the accumulated heat in the Earth system, with the land storing the second largest amount of this extra heat. Here, new estimates of continental heat storage are obtained, including changes in inland-water heat storage and permafrost heat storage in addition to changes in ground heat storage. We also argue that heat gains in all three components should be monitored independently of their magnitude due to heat-dependent processes affecting society and ecosystems.
Fulden Batibeniz, Mathias Hauser, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 14, 485–505, https://doi.org/10.5194/esd-14-485-2023, https://doi.org/10.5194/esd-14-485-2023, 2023
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We study single and concurrent heatwaves, droughts, precipitation, and wind extremes. Globally, these extremes become more frequent and affect larger land areas under future warming, with several countries experiencing extreme events every single month. Concurrent heatwaves–droughts (precipitation–wind) are projected to increase the most in mid–high-latitude countries (tropics). Every mitigation action to avoid further warming will reduce the number of people exposed to extreme weather events.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Sjoukje Y. Philip, Sarah F. Kew, Geert Jan van Oldenborgh, Faron S. Anslow, Sonia I. Seneviratne, Robert Vautard, Dim Coumou, Kristie L. Ebi, Julie Arrighi, Roop Singh, Maarten van Aalst, Carolina Pereira Marghidan, Michael Wehner, Wenchang Yang, Sihan Li, Dominik L. Schumacher, Mathias Hauser, Rémy Bonnet, Linh N. Luu, Flavio Lehner, Nathan Gillett, Jordis S. Tradowsky, Gabriel A. Vecchi, Chris Rodell, Roland B. Stull, Rosie Howard, and Friederike E. L. Otto
Earth Syst. Dynam., 13, 1689–1713, https://doi.org/10.5194/esd-13-1689-2022, https://doi.org/10.5194/esd-13-1689-2022, 2022
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In June 2021, the Pacific Northwest of the US and Canada saw record temperatures far exceeding those previously observed. This attribution study found such a severe heat wave would have been virtually impossible without human-induced climate change. Assuming no nonlinear interactions, such events have become at least 150 times more common, are about 2 °C hotter and will become even more common as warming continues. Therefore, adaptation and mitigation are urgently needed to prepare society.
Ryan S. Padrón, Lukas Gudmundsson, Laibao Liu, Vincent Humphrey, and Sonia I. Seneviratne
Biogeosciences, 19, 5435–5448, https://doi.org/10.5194/bg-19-5435-2022, https://doi.org/10.5194/bg-19-5435-2022, 2022
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The answer to how much carbon land ecosystems are projected to remove from the atmosphere until 2100 is different for each Earth system model. We find that differences across models are primarily explained by the annual land carbon sink dependence on temperature and soil moisture, followed by the dependence on CO2 air concentration, and by average climate conditions. Our insights on why each model projects a relatively high or low land carbon sink can help to reduce the underlying uncertainty.
Steven J. De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard L. Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
Earth Syst. Dynam., 13, 1305–1350, https://doi.org/10.5194/esd-13-1305-2022, https://doi.org/10.5194/esd-13-1305-2022, 2022
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Land cover and land management changes are important strategies for future land-based mitigation. We investigate the climate effects of cropland expansion, afforestation, irrigation, and wood harvesting using three Earth system models. Results show that these have important implications for surface temperature where the land cover and/or management change occurs and in remote areas. Idealized afforestation causes global warming, which might offset the cooling effect from enhanced carbon uptake.
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
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The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Fei Luo, Frank Selten, Kathrin Wehrli, Kai Kornhuber, Philippe Le Sager, Wilhelm May, Thomas Reerink, Sonia I. Seneviratne, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, and Dim Coumou
Weather Clim. Dynam., 3, 905–935, https://doi.org/10.5194/wcd-3-905-2022, https://doi.org/10.5194/wcd-3-905-2022, 2022
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Recent studies have identified the weather systems in observational data, where wave patterns with high-magnitude values that circle around the whole globe in either wavenumber 5 or wavenumber 7 are responsible for the extreme events. In conclusion, we find that the climate models are able to reproduce the large-scale atmospheric circulation patterns as well as their associated surface variables such as temperature, precipitation, and sea level pressure.
Shruti Nath, Quentin Lejeune, Lea Beusch, Sonia I. Seneviratne, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, https://doi.org/10.5194/esd-13-851-2022, 2022
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Uncertainty within climate model projections on inter-annual timescales is largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
Ronny Meier, Edouard L. Davin, Gordon B. Bonan, David M. Lawrence, Xiaolong Hu, Gregory Duveiller, Catherine Prigent, and Sonia I. Seneviratne
Geosci. Model Dev., 15, 2365–2393, https://doi.org/10.5194/gmd-15-2365-2022, https://doi.org/10.5194/gmd-15-2365-2022, 2022
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We revise the roughness of the land surface in the CESM climate model. Guided by observational data, we increase the surface roughness of forests and decrease that of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.
Lea Beusch, Zebedee Nicholls, Lukas Gudmundsson, Mathias Hauser, Malte Meinshausen, and Sonia I. Seneviratne
Geosci. Model Dev., 15, 2085–2103, https://doi.org/10.5194/gmd-15-2085-2022, https://doi.org/10.5194/gmd-15-2085-2022, 2022
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We introduce the first chain of computationally efficient Earth system model (ESM) emulators to translate user-defined greenhouse gas emission pathways into regional temperature change time series accounting for all major sources of climate change projection uncertainty. By combining the global mean emulator MAGICC with the spatially resolved emulator MESMER, we can derive ESM-specific and constrained probabilistic emulations to rapidly provide targeted climate information at the local scale.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
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Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Ulrike Proske, Verena Bessenbacher, Zane Dedekind, Ulrike Lohmann, and David Neubauer
Atmos. Chem. Phys., 21, 5195–5216, https://doi.org/10.5194/acp-21-5195-2021, https://doi.org/10.5194/acp-21-5195-2021, 2021
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Ice crystals falling out of one cloud can initiate freezing in a second cloud below. We estimate the occurrence frequency of this natural cloud seeding over Switzerland from satellite data and sublimation calculations. We find that such situations with an ice cloud above another cloud are frequent and that the falling crystals survive the fall between two clouds in a significant number of cases, suggesting that natural cloud seeding is an important phenomenon over Switzerland.
Quentin Lejeune, Edouard L. Davin, Grégory Duveiller, Bas Crezee, Ronny Meier, Alessandro Cescatti, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 1209–1232, https://doi.org/10.5194/esd-11-1209-2020, https://doi.org/10.5194/esd-11-1209-2020, 2020
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Trees are darker than crops or grasses; hence, they absorb more solar radiation. Therefore, land cover changes modify the fraction of solar radiation reflected by the land surface (its albedo), with consequences for the climate. We apply a new statistical method to simulations conducted with 15 recent climate models and find that albedo variations due to land cover changes since 1860 have led to a decrease in the net amount of energy entering the atmosphere by −0.09 W m2 on average.
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, https://doi.org/10.5194/essd-12-2959-2020, 2020
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We present an update of the IPCC WGI reference regions used in AR5 for the synthesis of climate change information. This revision was guided by the basic principles of climatic consistency and model representativeness (in particular for the new CMIP6 simulations). We also present a new dataset of monthly CMIP5 and CMIP6 spatially aggregated information using the new reference regions and describe a worked example of how to use this dataset to inform regional climate change studies.
Kathrin Wehrli, Mathias Hauser, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 855–873, https://doi.org/10.5194/esd-11-855-2020, https://doi.org/10.5194/esd-11-855-2020, 2020
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The 2018 summer was unusually hot for large areas in the Northern Hemisphere, and heatwaves on three continents led to major impacts on agriculture and society. This study investigates storylines for the extreme 2018 summer, given the observed atmospheric circulation but different levels of background global warming. The results reveal a strong contribution by the present-day level of global warming and show a dramatic outlook for similar events in a warmer climate.
Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta-Valero, Damien Desbruyères, Catia Domingues, Almudena García-García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian A. King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier Paolo Monselesan, Sarah Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald A. Slater, Andrea K. Steiner, Fiammetta Straneo, Mary-Louise Timmermans, and Susan E. Wijffels
Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, https://doi.org/10.5194/essd-12-2013-2020, 2020
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Understanding how much and where the heat is distributed in the Earth system is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to obtain the Earth heat inventory over the period 1960–2018.
Cited articles
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. a
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017. a
Balsamo, G., Agusti-Panareda, A., Albergel, C., Arduini, G., Beljaars, A.,
Bidlot, J., Blyth, E., Bousserez, N., Boussetta, S., Brown, A., Buizza, R.,
Buontempo, C., Chevallier, F., Choulga, M., Cloke, H., Cronin, M. F., Dahoui,
M., De Rosnay, P., Dirmeyer, P. A., Drusch, M., Dutra, E., Ek, M. B.,
Gentine, P., Hewitt, H., Keeley, S. P., Kerr, Y., Kumar, S., Lupu, C.,
Mahfouf, J.-F., McNorton, J., Mecklenburg, S., Mogensen, K., Muñoz-Sabater,
J., Orth, R., Rabier, F., Reichle, R., Ruston, B., Pappenberger, F., Sandu,
I., Seneviratne, S. I., Tietsche, S., Trigo, I. F., Uijlenhoet, R., Wedi, N.,
Woolway, R. I., and Zeng, X.: Satellite and In Situ Observations for
Advancing Global Earth Surface Modelling: A Review, Remote
Sens., 10, 2038, https://doi.org/10.3390/rs10122038, 2018. a, b
Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H.: Gaussian predictive
process models for large spatial data sets, J. Roy. Stat. Soc.-B, 70, 825–848, https://doi.org/10.1111/j.1467-9868.2008.00663.x, 2008. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and
Wedi, N. P.: The digital revolution of Earth-system science, Nature
Computational Science, 1, 104–113, https://doi.org/10.1038/s43588-021-00023-0,
2021a. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the
green transition, Nat. Clim. Change, 11, 80–83,
https://doi.org/10.1038/s41558-021-00986-y, 2021b. a, b
Bessenbacher, V., Seneviratne, S. I., and Gudmundsson, L.: CLIMFILL (v0.9), Zenodo [code], https://doi.org/10.5281/zenodo.6475578, 2021. a
Bhattacharjee, S. and Chen, J.: Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and
LULC Information, IEEE T. Geosci. Remote, 58, 8285–8300, https://doi.org/10.1109/TGRS.2020.2985047, 2020. a
Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a
learning tool to infer ordinary differential equation representations of
dynamical models, Nonlinear Proc. Geoph., 26, 143–162,
https://doi.org/10.5194/npg-26-143-2019, 2019. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data,
J. Geophys. Res.-Atmos., 119, 5128–5141,
https://doi.org/10.1002/2014JD021489, 2014. a, b
Brooks, E. B., Thomas, V. A., Wynne, R. H., and Coulston, J. W.: Fitting the
Multitemporal Curve: A Fourier Series Approach to the Missing
Data Problem in Remote Sensing Analysis, IEEE T. Geosci. Remote, 50, 3340–3353, https://doi.org/10.1109/TGRS.2012.2183137, 2012. a, b
Chiles, J.-P. and Delfiner, P.: Geostatistics: modeling spatial uncertainty,
Wiley series in probability and statistics, Wiley, Hoboken, N. J., 2nd edn., ISBN 9780470183151, 2012. a
Cowtan, K. and Way, R. G.: Coverage bias in the HadCRUT4 temperature series
and its impact on recent temperature trends, Q. J. Roy. Meteor. Soc., 140, 1935–1944, https://doi.org/10.1002/qj.2297, 2014. a, b, c
Cressie, N. and Wikle, C. K.: Statistics for spatio-temporal data, John Wiley
& Sons, ISBN 0471692743, 2015. a
Cressie, N., Frey, J., Harch, B., and Smith, M.: Spatial prediction on a river network, J. Agr. Biol. Envir. St., 11, 127–150, https://doi.org/10.1198/108571106X110649, 2006. a, b
Das, S., Roy, S., and Sambasivan, R.: Fast Gaussian Process Regression
for Big Data, Big Data Research, 14, 12–26,
https://doi.org/10.1016/j.bdr.2018.06.002, 2018. a, b
Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E.: Hierarchical
Nearest-Neighbor Gaussian Process Models for Large
Geostatistical Datasets, J. Am. Stat. Assoc., 111, 800–812, https://doi.org/10.1080/01621459.2015.1044091, 2016. a
Davenport, M. A. and Romberg, J.: An Overview of Low-Rank Matrix
Recovery From Incomplete Observations, IEEE J. Sel. Top. Signa., 10, 608–622, https://doi.org/10.1109/JSTSP.2016.2539100, 2016. 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., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
de Jeu, R. A. M., Wagner, W., Holmes, T. R. H., Dolman, A. J., van de Giesen,
N. C., and Friesen, J.: Global Soil Moisture Patterns Observed by
Space Borne Microwave Radiometers and Scatterometers, Surv. Geophys., 29, 399–420, https://doi.org/10.1007/s10712-008-9044-0, 2008. a
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi,
M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D.,
Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C.,
van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.:
ESA CCI Soil Moisture for improved Earth system understanding:
State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017. a, b, c
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2),
J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gelfand, A. E. and Schliep, E. M.: Spatial statistics and Gaussian processes:
A beautiful marriage, Spat. Stat., 18, 86–104,
https://doi.org/10.1016/j.spasta.2016.03.006, 2016. a
Gerber, F., Jong, R. d., Schaepman, M. E., Schaepman-Strub, G., and Furrer, R.:
Predicting Missing Values in Spatio-Temporal Remote Sensing
Data, IEEE T. Geosci. Remote, 56, 2841–2853,
https://doi.org/10.1109/TGRS.2017.2785240, 2018. a
Ghahramani, Z. and Jordan, M. I.: Learning from Incomplete Data, Tech.
rep., Defense Technical Information Center, https://doi.org/10.21236/ADA295618, 1994. a
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019. a, b
Gramacy, R. B. and Apley, D. W.: Local Gaussian Process Approximation for
Large Computer Experiments, J. Comput. Graph. Stat., 24, 561–578, https://doi.org/10.1080/10618600.2014.914442, 2015. a
Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., and
Seneviratne, S. I.: Global assessment of trends in wetting and drying over
land, Nat. Geosci., 7, 716–721, https://doi.org/10.1038/ngeo2247, 2014. a
Gruber, A., Dorigo, W. A., Crow, W., and Wagner, W.: Triple
Collocation-Based Merging of Satellite Soil Moisture
Retrievals, IEEE T. Geosci. Remote, 55,
6780–6792, https://doi.org/10.1109/TGRS.2017.2734070, 2017. a
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a
Gudmundsson, L. and Seneviratne, S. I.: Towards observation-based gridded runoff estimates for Europe, Hydrol. Earth Syst. Sci., 19, 2859–2879, https://doi.org/10.5194/hess-19-2859-2015, 2015. a, b, c
Gudmundsson, L., Boulange, J., Do, H. X., Gosling, S. N., Grillakis, M. G.,
Koutroulis, A. G., Leonard, M., Liu, J., Müller Schmied, H., Papadimitriou,
L., Pokhrel, Y., Seneviratne, S. I., Satoh, Y., Thiery, W., Westra, S.,
Zhang, X., and Zhao, F.: Globally observed trends in mean and extreme river
flow attributed to climate change, Science, 371, 1159–1162,
https://doi.org/10.1126/science.aba3996, 2021. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río,
J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy,
T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array
programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hauser, M.: Regionmask, https://regionmask.readthedocs.io/en/stable/ (last access: 7 June 2022), 2021. a
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D.,
and New, M.: A European daily high-resolution gridded data set of surface
temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008. a, b, c
Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi,
R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F.,
Nychka, D. W., Sun, F., and Zammit-Mangion, A.: A Case Study
Competition Among Methods for Analyzing Large Spatial Data,
J. Agr. Biol. Envir. St., 24, 398–425, https://doi.org/10.1007/s13253-018-00348-w, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d
Hirschi, M.: Using remotely sensed soil moisture for land–atmosphere coupling
diagnostics: The role of surface vs. root-zone soil moisture variability,
Remote Sens. Environ., 154, 246–252, https://doi.org/10.1016/j.rse.2014.08.030,
2014. a
Hoyer, S., Hamman, J., Roos, M., Cherian, D., Fitzgerald, C., Keewis, Fujii,
K., Maussion, F., Crusaderky, Kleeman, A., Clark, S., Kluyver, T., Hauser,
M., Munroe, J., Nicholas, T., Hatfield-Dodds, Z., Abernathey, R.,
MaximilianR, Wolfram, P. J., Alexamici, Signell, J., Sinai, Y. B., Helmus,
J. J., Mühlbauer, K., Markel, Rivera, G., Cable, P., Augspurger, T.,
Johnomotani, and Bovy, B.: pydata/xarray: v0.16.2,
https://doi.org/10.5281/ZENODO.598201, 2020. a
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and
Seneviratne, S. I.: Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage, Nature, 560, 628,
https://doi.org/10.1038/s41586-018-0424-4, 2018. a, b
Hunter, J. D.: Matplotlib: A 2D Graphics Environment, Computing in
Science & Engineering, 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a, b, c, d, e
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a, b
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D.,
Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A.,
Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations, J. Geophys. Res., 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011. a, b
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. a, b
Kadow, C., Hall, D. M., and Ulbrich, U.: Artificial intelligence reconstructs
missing climate information, Nat. Geosci., 13, 408–413,
https://doi.org/10.1038/s41561-020-0582-5, 2020. a
Landerer, F.: GRACE & GRACE-FO – Data Months/Days, JPL [data set], https://grace.jpl.nasa.gov/data/grace-months/ (last access: 7 June 2022), 2021. a
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial
water storage estimates, Water Resour. Res., 48, W04531,
https://doi.org/10.1029/2011WR011453, 2012. a
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood,
E. F.: Inroads of remote sensing into hydrologic science during the WRR
era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616,
2015. a, b
Lin, J.: Divergence measures based on the Shannon entropy, IEEE T. Inform. Theory, 37, 145–151, https://doi.org/10.1109/18.61115, 1991. a
Little, R. J. A. and Rubin, D. B.: Missing Data in Experiments, in:
Statistical Analysis with Missing Data, John Wiley & Sons Ltd, 24–40, https://doi.org/10.1002/9781119013563.ch2, 2014. a
Liu, T., Wei, H., and Zhang, K.: Wind power prediction with missing data using
Gaussian process regression and multiple imputation, Appl. Soft Comput., 71, 905–916, https://doi.org/10.1016/j.asoc.2018.07.027, 2018. a, b
Mariethoz, G., McCabe, M. F., and Renard, P.: Spatiotemporal reconstruction of
gaps in multivariate fields using the direct sampling approach:
Reconstruction of gaps using direct sampling, Water Resour. Res., 48, W10507, https://doi.org/10.1029/2012WR012115, 2012. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b, c
Martens, B., Schumacher, D. L., Wouters, H., Muñoz-Sabater, J., Verhoest, N. E. C., and Miralles, D. G.: Evaluating the land-surface energy partitioning in ERA5, Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020, 2020. a
Mazumder, R., Hastie, T., and Tibshirani, R.: Spectral Regularization
Algorithms for Learning Large Incomplete Matrices, J.
Mach. Learn. Res., 1, 2287–2322, PMID: 21552465, 2010. a
Miralles, D. G., Gentine, P., Seneviratne, S., and Teuling, A. J.:
Land-atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, An. NY Acad. Sci.,
1436, 19–35, https://doi.org/10.1111/nyas.13912, 2019. a
Mueller, B. and Seneviratne, S. I.: Hot days induced by precipitation deficits
at the global scale, Proc. Natl. Acad. Sci., 109,
12398–12403, https://doi.org/10.1073/pnas.1204330109, 2012. a
Nicolai-Shaw, N., Gudmundsson, L., Hirschi, M., and Seneviratne, S. I.:
Long-term predictability of soil moisture dynamics at the global scale:
Persistence versus large-scale drivers, Geophys. Res. Lett., 43,
8554–8562, https://doi.org/10.1002/2016GL069847, 2016. a
Nicolai-Shaw, N., Zscheischler, J., Hirschi, M., Gudmundsson, L., and
Seneviratne, S. I.: A drought event composite analysis using satellite
remote-sensing based soil moisture, Remote Sens. Environ., 203,
216–225, https://doi.org/10.1016/j.rse.2017.06.014, 2017. a
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. a
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M.,
Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J.,
Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron,
O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D.,
Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P.,
Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C.,
Cremonese, E., Curtis, P. S., D’Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. d., Ligne, A. D., De Oliveira,
R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. d., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne,
E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S.,
Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B.,
Gitelson, A., Goded, I., Goeckede, M., Goldstein, A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening, C., Grünwald,
T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson, C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera, N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt,
J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch,
W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J.-M., Lion, M., Liska, A. J., Lohila,
A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V.,
Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes,
J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S.,
Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck,
J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival,
J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N.,
Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, , Raz-Yaseef, N., Rebmann, C., Reed, D., Dios, V. R. d., Restrepo-
Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Cañete, E. P., Sanchez-Mejia, Z. M., Schmid,
H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I.,
Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T.,
Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel,
E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang,
H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G.,
Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 dataset and
the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 27, https://doi.org/10.1038/s41597-020-0534-3, 2020. a, b
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, Journal of Mach. Learn.
Research, 12, 2825–2830, http://jmlr.org/papers/v12/pedregosa11a.html (last access: 7 June 2022), 2011. a
Ridder, N. N., Pitman, A. J., Westra, S., Ukkola, A., Do, H. X., Bador, M.,
Hirsch, A. L., Evans, J. P., Di Luca, A., and Zscheischler, J.: Global
hotspots for the occurrence of compound events, Nat. Commun., 11,
5956, https://doi.org/10.1038/s41467-020-19639-3, 2020. a
Sahoo, A. K., De Lannoy, G. J., Reichle, R. H., and Houser, P. R.: Assimilation
and downscaling of satellite observed soil moisture over the Little River
ExperimentalWatershed in Georgia, USA, Adv. Water Resour.,
52, 19–33, https://doi.org/10.1016/j.advwatres.2012.08.007, 2013. 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, c, d, e, f, g
Shen, H. and Zhang, L.: A MAP-Based Algorithm for Destriping and
Inpainting of Remotely Sensed Images, IEEE T. Geosci. Remote S., 47, 1492–1502, https://doi.org/10.1109/TGRS.2008.2005780, 2009. a, b, c
Shen, H., Li, X., Cheng, Q., Zeng, C., Yang, G., Li, H., and Zhang, L.: Missing
Information Reconstruction of Remote Sensing Data: A Technical
Review, IEEE Geosci. Remote S. Mag., 3, 61–85,
https://doi.org/10.1109/MGRS.2015.2441912, 2015. a, b
Stekhoven, D. J. and Bühlmann, P.: MissForest–non-parametric missing value
imputation for mixed-type data, Bioinformatics (Oxford, England), 28,
112–118, https://doi.org/10.1093/bioinformatics/btr597, 2012. a, b, c, d
Swenson, S. and Wahr, J.: Post-processing removal of correlated errors in
GRACE data, Geophys. Res. Lett., 33, L08402,
https://doi.org/10.1029/2005GL025285, 2006. a
Swenson, S. C.: GRACE Montly Land and Water Mass Grids NetCDF
Release 5.0. Ver. 5.0. PO.DAAC, CA, USA., NASA JPL,
https://doi.org/10.5067/TELND-NC005, 2012. a
Tang, F. and Ishwaran, H.: Random forest missing data algorithms, Statistical
Analysis and Data Mining: The ASA Data Science Journal, 10, 363–377,
https://doi.org/10.1002/sam.11348, 2017. a
Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrol. Earth Syst. Sci., 24, 2527–2544, https://doi.org/10.5194/hess-24-2527-2020, 2020. a
Teuling, A. J., Seneviratne, S. I., Stockli, R., Reichstein, M., Moors, E.,
Ciais, P., Luyssaert, S., van den Hurk, B., Ammann, C., Bernhofer, C.,
Dellwik, E., Gianelle, D., Gielen, B., Grunwald, T., Klumpp, K., Montagnani,
L., Moureaux, C., Sottocornola, M., and Wohlfahrt, G.: Contrasting response
of European forest and grassland energy exchange to heatwaves, Nature
Geosci., 3, 722–727, https://doi.org/10.1038/ngeo950, 2010. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy
1.0: Fundamental Algorithms for Scientific Computing in Python,
Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vogel, M. M., Orth, R., Cheruy, F., Hagemann, S., Lorenz, R., 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.
a, b
von Buttlar, J., Zscheischler, J., and Mahecha, M. D.: An extended approach for
spatiotemporal gapfilling: dealing with large and systematic gaps in
geoscientific datasets, Nonlinear Proc. Geoph., 21, 203–215,
https://doi.org/10.5194/npg-21-203-2014, 2014. a, b
Wan, Z., Hook, S., and Hulley, G.: MYD11C1 MODIS/Aqua Land Surface
Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006, MODIS [data set], https://doi.org/10.5067/MODIS/MYD11C1.006, 2015. a
Wang, Y. and Chaib-draa, B.: An online Bayesian filtering framework for
Gaussian process regression: Application to global surface temperature
analysis, Expert Syst. Appl., 67, 285–295,
https://doi.org/10.1016/j.eswa.2016.09.018, 2017. a
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S.:
Identifying Key Driving Processes of Major Recent Heat Waves,
J. Geophys. Res.-Atmos., 124, 11746–11765,
https://doi.org/10.1029/2019JD030635, 2019. a, b, c
Zeng, C., Shen, H., Zhong, M., Zhang, L., and Wu, P.: Reconstructing MODIS
LST Based on Multitemporal Classification and Robust Regression,
IEEE Geosci. Remote S. Lett., 12, 512–516,
https://doi.org/10.1109/LGRS.2014.2348651, 2015. a, b
Zhan, X., Zheng, W., Fang, L., Liu, J., Hain, C., Yin, J., and Ek, M.: A
preliminary assessment of the impact of SMAP Soil Moisture on numerical
weather Forecasts from GFS and NUWRF models, 2016 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), p. 4, ISSN 2153–7003,
https://doi.org/10.1109/IGARSS.2016.7730362, 2016. a
Zhang, L., Liu, Y., Ren, L., Teuling, A. J., Zhang, X., Jiang, S., Yang, X.,
Wei, L., Zhong, F., and Zheng, L.: Reconstruction of ESA CCI
satellite-derived soil moisture using an artificial neural network
technology, Sci. Total Environ., 782, 146602,
https://doi.org/10.1016/j.scitotenv.2021.146602, 2021. a
Zhang, Q., Yuan, Q., Zeng, C., Li, X., and Wei, Y.: Missing Data
Reconstruction in Remote Sensing Image With a Unified
Spatial-Temporal-Spectral Deep Convolutional Neural Network,
IEEE T. Geosci. Remote, 56, 4274–4288,
https://doi.org/10.1109/TGRS.2018.2810208, 2018. a
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Earth observations have many missing values. They are often filled using information from...