Articles | Volume 21, issue 23
https://doi.org/10.5194/bg-21-5517-2024
© 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/bg-21-5517-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Dec 2024
Research article |
| 12 Dec 2024
| 12 Dec 2024
On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Anna Ukkola
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Sanaa Hobeichi
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Jon Cranko Page
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Mathew Lipson
Bureau of Meteorology, Melbourne, Australia
Martin G. De Kauwe
School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK
Samuel Green
CLEX, UNSW Sydney, Sydney, Australia
CCRC, UNSW Sydney, Sydney, Australia
Claire Brenner
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Jonathan Frame
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Grey Nearing
Google Research, Mountain View, CA, USA
Martyn Clark
Department of Civil Engineering, University of Calgary, Calgary, Canada
Martin Best
UKMO, Exeter, UK
Peter Anthoni
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Gabriele Arduini
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Souhail Boussetta
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Silvia Caldararu
Max Planck Institute for Biogeochemistry, Jena, Germany
Discipline of Botany, School of Natural Sciences, trinity College Dublin, Dublin, Ireland
Kyeungwoo Cho
Department of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea
Matthias Cuntz
INRAE, Nancy, France
David Fairbairn
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Craig R. Ferguson
Atmospheric Sciences Research Center, University at Albany, State University of New York, Albany, NY, USA
Hyungjun Kim
HydroKlima Lab, KAIST, Daejeon, South Korea
Yeonjoo Kim
Department of Civil and Environmental Engineering, Yonsei University, Seoul, South Korea
Jürgen Knauer
CSIRO Environment, Canberra, Australia
Hawkesbury Institute for the Environment, Western Sydney University, Sydney, Australia
David Lawrence
NCAR, Boulder, CO, USA
Xiangzhong Luo
Department of Geography, National University of Singapore, Singapore
Sergey Malyshev
NOAA GFDL, Princeton, NJ, USA
Tomoko Nitta
Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Jerome Ogee
INRAE, Nancy, France
Keith Oleson
NCAR, Boulder, CO, USA
Catherine Ottlé
LSCE, Paris, France
Phillipe Peylin
LSCE, Paris, France
Patricia de Rosnay
European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Heather Rumbold
UKMO, Exeter, UK
Bob Su
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
Nicolas Vuichard
LSCE, Paris, France
Anthony P. Walker
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Xiaoni Wang-Faivre
LSCE, Paris, France
Yunfei Wang
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
Yijian Zeng
Faculty of Geo-Information Science and Earth Observation, University of Twente, the Netherlands
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Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
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Inne Vanderkelen, Marie-Estelle Demoury, Sean Swenson, David M. Lawrence, Benjamin D. Stocker, Myke Koopmans, and Édouard L. Davin
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Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
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Amali A. Amali, Clemens Schwingshackl, Akihiko Ito, Alina Barbu, Christine Delire, Daniele Peano, David M. Lawrence, David Wårlind, Eddy Robertson, Edouard L. Davin, Elena Shevliakova, Ian N. Harman, Nicolas Vuichard, Paul A. Miller, Peter J. Lawrence, Tilo Ziehn, Tomohiro Hajima, Victor Brovkin, Yanwu Zhang, Vivek K. Arora, and Julia Pongratz
Earth Syst. Dynam., 16, 803–840, https://doi.org/10.5194/esd-16-803-2025, https://doi.org/10.5194/esd-16-803-2025, 2025
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Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 29, 2361–2375, https://doi.org/10.5194/hess-29-2361-2025, https://doi.org/10.5194/hess-29-2361-2025, 2025
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Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
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Martin Richard Willett, Melissa Brooks, Andrew Bushell, Paul Earnshaw, Samantha Smith, Lorenzo Tomassini, Martin Best, Ian Boutle, Jennifer Brooke, John M. Edwards, Kalli Furtado, Catherine Hardacre, Andrew J. Hartley, Alan Hewitt, Ben Johnson, Adrian Lock, Andy Malcolm, Jane Mulcahy, Eike Müller, Heather Rumbold, Gabriel G. Rooney, Alistair Sellar, Masashi Ujiie, Annelize van Niekerk, Andy Wiltshire, and Michael Whitall
EGUsphere, https://doi.org/10.5194/egusphere-2025-1829, https://doi.org/10.5194/egusphere-2025-1829, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA8GL9, which includes improvements to the represenation of convection and other physical processes. GA8GL9 is used for operational weather prediction in the UK and forms the basis for the next GA and GL configuration.
Tanguy Postic, François de Coligny, Isabelle Chuine, Louis Devresse, Daniel Berveiller, Hervé Cochard, Matthias Cuntz, Nicolas Delpierre, Émilie Joetzjer, Jean-Marc Limousin, Jean-Marc Ourcival, François Pimont, Julien Ruffault, Guillaume Simioni, Nicolas K. Martin-StPaul, and Xavier Morin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2110, https://doi.org/10.5194/egusphere-2025-2110, 2025
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PHOREAU is a forest dynamic model that links plant traits with water use, growth, and climate responses to explore how species diversity affects productivity and resilience. Validated across European forests, PHOREAU simulates how tree communities function under drought and warming. Our findings support the use of trait-based modeling to guide forest adaptation strategies under future climate scenarios.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
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The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Cheng Gong, Yan Wang, Hanqin Tian, Sian Kou-Giesbrecht, Nicolas Vuichard, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2025-1416, https://doi.org/10.5194/egusphere-2025-1416, 2025
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Our results showed substantially varied fertilizer-induced soil NOx emissions in 2019 from 0.84 to 2.2 Tg N yr-1 globally. Such variations further lead to 0.3 to 3.3 ppbv summertime ozone enhancement in agricultural hotspot regions and 7.1 ppbv to 16.6 ppbv reductions in global methane concentrations
Qianqian Han, Yijian Zeng, Yunfei Wang, Fakhereh Sarah Alidoost, Francesco Nattino, Yang Liu, and Bob Su
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-183, https://doi.org/10.5194/essd-2025-183, 2025
Revised manuscript under review for ESSD
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Understanding how land interacts with the atmosphere is crucial for studying climate change, yet global high-resolution data on energy, water, and carbon exchanges remain limited. This study introduces a new dataset that estimates these exchanges hourly from 2000 to 2020 by combining physical process model, field measurements, and machine learning with satellite and meteorological data. Our dataset provides valuable insights into how ecosystems respond to climate extremes worldwide.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
Geosci. Model Dev., 18, 2275–2301, https://doi.org/10.5194/gmd-18-2275-2025, https://doi.org/10.5194/gmd-18-2275-2025, 2025
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This study shows the significance of groundwater in resolving the coupled terrestrial water–energy cycle. LM4-SHARC (soil–hillslope aquifer–river continuum) describes the hillslope groundwater using its emergent properties, yielding noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implications of groundwater-mediated hydrologic interactions between hillslopes and streams need further exploration in the Earth system modeling community.
Tea Thum, Tuuli Miinalainen, Outi Seppälä, Holly Croft, Cheryl Rogers, Ralf Staebler, Silvia Caldararu, and Sönke Zaehle
Biogeosciences, 22, 1781–1807, https://doi.org/10.5194/bg-22-1781-2025, https://doi.org/10.5194/bg-22-1781-2025, 2025
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Climate change has the potential to influence the carbon sequestration potential of terrestrial ecosystems, and here the nitrogen cycle is also important. We used the terrestrial biosphere model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) in a mixed deciduous forest in Canada. We investigated the usefulness of using the leaf area index and leaf chlorophyll content to improve the parameterization of the model. This work paves the way for using spaceborne observations in model parameterizations, also including information on the nitrogen cycle.
Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Deborah Cohen, and Oren Gilon
EGUsphere, https://doi.org/10.5194/egusphere-2025-1224, https://doi.org/10.5194/egusphere-2025-1224, 2025
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Missing input data are one of the most common challenges when building deep learning hydrological models. We present and analyze different methods that can produce predictions when certain inputs are missing during training or inference. Our proposed strategies provide high accuracy while allowing for more flexible data handling and being robust to outages in operational scenarios.
Enrico Zorzetto, Paul Ginoux, Sergey Malyshev, and Elena Shevliakova
The Cryosphere, 19, 1313–1334, https://doi.org/10.5194/tc-19-1313-2025, https://doi.org/10.5194/tc-19-1313-2025, 2025
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Light-absorbing particle (LAP) deposition on snow leads to a darkening of the snow surface and can thus accelerate snow melt. Understanding the extent to which different types of LAPs contribute to snow melt is important to both predict changes in water availability and improve global climate model predictions. Here, we extend a recently developed snow model to account for the deposition of LAPs in the snowpack and evaluate the effect of snow darkening on accelerating snow melt.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Wouter J. M. Knoben, Kasra Keshavarz, Laura Torres-Rojas, Cyril Thébault, Nathaniel W. Chaney, Alain Pietroniro, and Martyn P. Clark
EGUsphere, https://doi.org/10.5194/egusphere-2025-893, https://doi.org/10.5194/egusphere-2025-893, 2025
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Many existing data sets for hydrologic analysis tend treat catchments as single, spatially homogeneous units, focus on daily data and typically do not support more complex models. This paper introduces a data set that goes beyond this setup by: (1) providing data at higher spatial and temporal resolution, (2) specifically considering the data requirements of all common hydrologic model types, (3) using statistical summaries of the data aimed at quantifying spatial and temporal heterogeneity.
Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek
Atmos. Chem. Phys., 25, 2311–2331, https://doi.org/10.5194/acp-25-2311-2025, https://doi.org/10.5194/acp-25-2311-2025, 2025
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This study derives a gridded dust emission dataset for 1841–2000 by employing a combination of observed dust from core records and reanalyzed global dust cycle constraints. We evaluate the ability of global models to replicate the observed historical dust variability by using the emission dataset to force a historical simulation in an Earth system model. We show that prescribing our emissions forces the model to better match observations than other mechanistic models.
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025, https://doi.org/10.5194/gmd-18-921-2025, 2025
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We compared spatiotemporal forecasts of three machine learning models that learned water and energy
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Maureen Beaudor, Didier Hauglustaine, Juliette Lathière, Martin Van Damme, Lieven Clarisse, and Nicolas Vuichard
Atmos. Chem. Phys., 25, 2017–2046, https://doi.org/10.5194/acp-25-2017-2025, https://doi.org/10.5194/acp-25-2017-2025, 2025
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Agriculture is the biggest ammonia (NH3) source, impacting air quality, climate, and ecosystems. Because of food demand, NH3 emissions are projected to rise by 2100. Using a global model, we analyzed the impact of present and future NH3 emissions generated from a land model. Our results show improved ammonia patterns compared to a reference inventory. Future scenarios predict up to 70 % increase in global NH3 burden, with significant changes in radiative forcing that can greatly elevate N2O.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
EGUsphere, https://doi.org/10.5194/egusphere-2024-4024, https://doi.org/10.5194/egusphere-2024-4024, 2025
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Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that while there have been widespread decreases in droughts since the beginning of the 20th century. However, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter Julien Rayner, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2025-109, https://doi.org/10.5194/egusphere-2025-109, 2025
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Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data is a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical method named 4DEnVar to calibrate parameters of a land surface model using this data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
Jungho Seo, Mahdi Panahi, Ji Hyun Kim, Sayed M. Bateni, and Yeonjoo Kim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-349, https://doi.org/10.5194/essd-2024-349, 2025
Manuscript not accepted for further review
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This study introduces a machine learning-based daily global gridded snow water equivalent (SWE) product (SWEML) at 0.25° from 1980 to 2020. Comparison of SWEML to other global SWE datasets showed that SWEML exhibited high accuracy, especially in mountainous and high-elevation areas, such as the Rocky Mountains in the US. In addition, SWEML effectively depicted snow accumulation during winters and snow melting during springs.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
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The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Luis-Enrique Olivera-Guerra, Catherine Ottlé, Nina Raoult, and Philippe Peylin
Hydrol. Earth Syst. Sci., 29, 261–290, https://doi.org/10.5194/hess-29-261-2025, https://doi.org/10.5194/hess-29-261-2025, 2025
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We assimilate the recent ESA-CCI land surface temperature (LST) product to optimize parameters of a land surface model (ORCHIDEE). We test different assimilation strategies to evaluate the best strategy over various in situ stations across Europe. We also provide advice on how to assimilate this LST product to better simulate LST and surface energy fluxes. Finally, we demonstrate the effectiveness of this optimization, which is essential to better simulate future projections.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Ruiying Zhao, Xiangzhong Luo, Yuheng Yang, Luri Nurlaila Syahid, Chi Chen, and Janice Ser Huay Lee
Biogeosciences, 21, 5393–5406, https://doi.org/10.5194/bg-21-5393-2024, https://doi.org/10.5194/bg-21-5393-2024, 2024
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Southeast Asia has been a global hot spot of land-use change over the past 50 years. Meanwhile, it also hosts some of the most carbon-dense and diverse ecosystems in the world. Here, we explore the impact of land-use change, along with other environmental factors, on the ecosystem in Southeast Asia. We find that elevated CO2 imposed a positive impact on vegetation greenness, but the positive impact was largely offset by intensive land-use changes in the region, particularly cropland expansion.
Sven Armin Westermann, Anke Hildebrandt, Souhail Bousetta, and Stephan Thober
Biogeosciences, 21, 5277–5303, https://doi.org/10.5194/bg-21-5277-2024, https://doi.org/10.5194/bg-21-5277-2024, 2024
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Plants at the land surface mediate between soil and the atmosphere regarding water and carbon transport. Since plant growth is a dynamic process, models need to consider these dynamics. Two models that predict water and carbon fluxes by considering plant temporal evolution were tested against observational data. Currently, dynamizing plants in these models did not enhance their representativeness, which is caused by a mismatch between implemented physical relations and observable connections.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Benjamin Mark Sanderson, Victor Brovkin, Rosie Fisher, David Hohn, Tatiana Ilyina, Chris Jones, Torben Koenigk, Charles Koven, Hongmei Li, David Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew Macdougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Seferian, Lori Sentman, Isla Simpson, Chris Smith, Norman Steinert, Abigail Swann, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3356, https://doi.org/10.5194/egusphere-2024-3356, 2024
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This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated zero emissions commitment due to emissions rates exceeding historical levels.
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, Xavier Fettweis, and Philippe Conesa
The Cryosphere, 18, 5067–5099, https://doi.org/10.5194/tc-18-5067-2024, https://doi.org/10.5194/tc-18-5067-2024, 2024
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The evolution of the Greenland ice sheet is highly dependent on surface melting and therefore on the processes operating at the snow–atmosphere interface and within the snow cover. Here we present new developments to apply a snow model to the Greenland ice sheet. The performance of this model is analysed in terms of its ability to simulate ablation processes. Our analysis shows that the model performs well when compared with the MAR regional polar atmospheric model.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Zengjing Song, Yijian Zeng, Yunfei Wang, Enting Tang, Danyang Yu, Fakhereh Alidoost, Mingguo Ma, Xujun Han, Xuguang Tang, Zhongjing Zhu, Yao Xiao, Debing Kong, and Zhongbo Su
EGUsphere, https://doi.org/10.5194/egusphere-2024-2940, https://doi.org/10.5194/egusphere-2024-2940, 2024
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The exchange of water and carbon between the plant and the atmosphere is affected under water stress conditions. In this study, a leaf-water-potential-based water stress factor is considered in the STEMMUS-SCOPE (hereafter STEMMUS-SCOPE-PHS), to replace the conventional soil-moisture-based water stress factor. The results show that leaf water potential reflects the plant water stress well, and the STEMMUS-SCOPE-PHS outperforms STEMMUS-SCOPE in the dynamics of the water, energy and carbon fluxes.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
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Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
Jaime A. Riano Sanchez, Nicolas Vuichard, and Philippe Peylin
Earth Syst. Dynam., 15, 1227–1253, https://doi.org/10.5194/esd-15-1227-2024, https://doi.org/10.5194/esd-15-1227-2024, 2024
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We quantify the projected change in land carbon store (CLCS) for different socioeconomic scenarios (SSPs). Using factorial simulations of a land surface model, we estimate the CLCS uncertainties associated with land use change (LUC) and nitrogen (N) deposition trajectories. Our study highlights the need for delivering additional LUC and N deposition trajectories from integrated assessment models for each SSP in order to accurately assess their impacts on the carbon cycle and climate.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024, https://doi.org/10.5194/hess-28-4099-2024, 2024
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This work examines the impact of temporal and spatial information on the uncertainty estimation of streamflow forecasts. The study emphasizes the importance of data updates and global information for precise uncertainty estimates. We use conformal prediction to show that recent data enhance the estimates, even if only available infrequently. Local data yield reasonable average estimations but fall short for peak-flow events. The use of global data significantly improves these predictions.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
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Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Daniel Klotz, Martin Gauch, Frederik Kratzert, Grey Nearing, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3665–3673, https://doi.org/10.5194/hess-28-3665-2024, https://doi.org/10.5194/hess-28-3665-2024, 2024
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The evaluation of model performance is essential for hydrological modeling. Using performance criteria requires a deep understanding of their properties. We focus on a counterintuitive aspect of the Nash–Sutcliffe efficiency (NSE) and show that if we divide the data into multiple parts, the overall performance can be higher than all the evaluations of the subsets. Although this follows from the definition of the NSE, the resulting behavior can have unintended consequences in practice.
Anna M. Ukkola, Steven Thomas, Elisabeth Vogel, Ulrike Bende-Michl, Steven Siems, Vjekoslav Matic, and Wendy Sharples
EGUsphere, https://doi.org/10.31223/X56110, https://doi.org/10.31223/X56110, 2024
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Future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain. We assess future drought changes in Australia using projections from climate and hydrological models. We show an increasing probability of drought over highly-populated and agricultural regions of Australia in coming decades, suggesting potential impacts on agricultural activities, ecosystems and urban water supply.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-1646, https://doi.org/10.5194/egusphere-2024-1646, 2024
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While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
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Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Yunfei Wang, Yijian Zeng, Zengjing Song, Danyang Yu, Qianqian Han, Enting Tang, Henk de Bruin, and Zhongbo Su
EGUsphere, https://doi.org/10.5194/egusphere-2024-1321, https://doi.org/10.5194/egusphere-2024-1321, 2024
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Various methods were proposed to estimate irrigation water requirements (IWR). However, the simulated IWR exhibits large differences. This study evaluates six potential evapotranspiration (PET) methods and proposes a practical approach to estimate IWR. The radiation-based methods show promise in approximating daily PET accurately, and the STEMMUS-SCOPE model can reliably estimate IWR. This research enhances our understanding of different PET methods and their implications for water management.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
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Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
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We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
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Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Danny M. Leung, Jasper F. Kok, Longlei Li, Natalie M. Mahowald, David M. Lawrence, Simone Tilmes, Erik Kluzek, Martina Klose, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 24, 2287–2318, https://doi.org/10.5194/acp-24-2287-2024, https://doi.org/10.5194/acp-24-2287-2024, 2024
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This study uses a premier Earth system model to evaluate a new desert dust emission scheme proposed in our companion paper. We show that our scheme accounts for more dust emission physics, hence matching better against observations than other existing dust emission schemes do. Our scheme's dust emissions also couple tightly with meteorology, hence likely improving the modeled dust sensitivity to climate change. We believe this work is vital for improving dust representation in climate models.
Enting Tang, Yijian Zeng, Yunfei Wang, Zengjing Song, Danyang Yu, Hongyue Wu, Chenglong Qiao, Christiaan van der Tol, Lingtong Du, and Zhongbo Su
Biogeosciences, 21, 893–909, https://doi.org/10.5194/bg-21-893-2024, https://doi.org/10.5194/bg-21-893-2024, 2024
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Our study shows that planting shrubs in a semiarid grassland reduced the soil moisture and increased plant water uptake and transpiration. Notably, the water used by the ecosystem exceeded the rainfall received during the growing seasons, indicating an imbalance in the water cycle. The findings demonstrate the effectiveness of the STEMMUS–SCOPE model as a tool to represent ecohydrological processes and highlight the need to consider energy and water budgets for future revegetation projects.
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, and Tim Palmer
Hydrol. Earth Syst. Sci., 27, 4661–4685, https://doi.org/10.5194/hess-27-4661-2023, https://doi.org/10.5194/hess-27-4661-2023, 2023
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Lakes play an important role when we try to explain and predict the weather. More accurate and up-to-date description of lakes all around the world for numerical models is a continuous task. However, it is difficult to assess the impact of updated lake description within a weather prediction system. In this work, we develop a method to quickly and automatically define how, where, and when updated lake description affects weather prediction.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Diogo Costa, Kyle Klenk, Wouter Knoben, Andrew Ireson, Raymond J. Spiteri, and Martyn Clark
EGUsphere, https://doi.org/10.5194/egusphere-2023-2787, https://doi.org/10.5194/egusphere-2023-2787, 2023
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This work helps improve water quality simulations in aquatic ecosystems through a new modeling concept, which we termed “OpenWQ”. It allows tailoring biogeochemistry calculations and integration with existing hydrological (water quantity) simulation tools. The integration is demonstrated with two hydrological models. The models were tested for different pollution scenarios. This paper helps improve interoperability, transparency, flexibility, and reproducibility in water quality simulations.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Catherine Ottlé, and Frédérique Cheruy
The Cryosphere, 17, 5095–5130, https://doi.org/10.5194/tc-17-5095-2023, https://doi.org/10.5194/tc-17-5095-2023, 2023
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This study investigates the impact of topography on snow cover parameterizations using models and observations. Parameterizations without topography-based considerations overestimate snow cover. Incorporating topography reduces snow overestimation by 5–10 % in mountains, in turn reducing cold biases. However, some biases remain, requiring further calibration and more data. Assessing snow cover parameterizations is challenging due to limited and uncertain data in mountainous regions.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri
Earth Syst. Dynam., 14, 1239–1259, https://doi.org/10.5194/esd-14-1239-2023, https://doi.org/10.5194/esd-14-1239-2023, 2023
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Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
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We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Yuan Yan, Anne Klosterhalfen, Fernando Moyano, Matthias Cuntz, Andrew C. Manning, and Alexander Knohl
Biogeosciences, 20, 4087–4107, https://doi.org/10.5194/bg-20-4087-2023, https://doi.org/10.5194/bg-20-4087-2023, 2023
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A better understanding of O2 fluxes, their exchange ratios with CO2 and their interrelations with environmental conditions would provide further insights into biogeochemical ecosystem processes. We, therefore, used the multilayer canopy model CANVEG to simulate and analyze the flux exchange for our forest study site for 2012–2016. Based on these simulations, we further successfully tested the application of various micrometeorological methods and the prospects of real O2 flux measurements.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
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Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, and Yijian Zeng
Hydrol. Earth Syst. Sci., 27, 2579–2590, https://doi.org/10.5194/hess-27-2579-2023, https://doi.org/10.5194/hess-27-2579-2023, 2023
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Our knowledge on sources and dynamics of rock moisture is limited. By using frequency domain reflectometry (FDR), we monitored rock moisture in a cave. The results of an explainable deep learning model reveal that the direct source of rock moisture responsible for weathering in the studied cave is vapour, not infiltrating precipitation. A physics-informed deep learning model, which uses variables controlling vapor condensation as model inputs, leads to accurate rock water content predictions.
Nina Raoult, Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, and Vladislav Bastrikov
The Cryosphere, 17, 2705–2724, https://doi.org/10.5194/tc-17-2705-2023, https://doi.org/10.5194/tc-17-2705-2023, 2023
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Greenland ice sheet melting due to global warming could significantly impact global sea-level rise. The ice sheet's albedo, i.e. how reflective the surface is, affects the melting speed. The ORCHIDEE computer model is used to simulate albedo and snowmelt to make predictions. However, the albedo in ORCHIDEE is lower than that observed using satellites. To correct this, we change model parameters (e.g. the rate of snow decay) to reduce the difference between simulated and observed values.
Claudia Voigt, Anne Alexandre, Ilja M. Reiter, Jean-Philippe Orts, Christine Vallet-Coulomb, Clément Piel, Jean-Charles Mazur, Julie C. Aleman, Corinne Sonzogni, Helene Miche, and Jérôme Ogée
Biogeosciences, 20, 2161–2187, https://doi.org/10.5194/bg-20-2161-2023, https://doi.org/10.5194/bg-20-2161-2023, 2023
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Data on past relative humidity (RH) ARE needed to improve its representation in Earth system models. A novel isotope parameter (17O-excess) of plant silica has been developed to quantify past RH. Using comprehensive monitoring and novel methods, we show how environmental and plant physiological parameters influence the 17O-excess of plant silica and leaf water, i.e. its source water. The insights gained from this study will help to improve estimates of RH from fossil plant silica deposits.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023, https://doi.org/10.5194/acp-23-6487-2023, 2023
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Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023, https://doi.org/10.5194/esd-14-549-2023, 2023
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Studies analyzing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases that translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but that temporal properties do not improve.
Lianyu Yu, Yijian Zeng, Huanjie Cai, Mengna Li, Yuanyuan Zha, Jicai Zeng, Hui Qian, and Zhongbo Su
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-221, https://doi.org/10.5194/gmd-2022-221, 2023
Revised manuscript not accepted
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We developed a coupled soil water-groundwater (SW-GW) model, which is verified as physically accurate and applicable in large-scale groundwater problems. The role of vadose zone processes, coupling approach, and spatiotemporal heterogeneity of SW-GW interactions were highlighted as essential to represent the SW-GW system. Given the relevant dataset, the developed SW-GW modeling framework has the potential to portray the processes "from bedrock to atmosphere" in a physically consistent manner.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Heather S. Rumbold, Richard J. J. Gilham, and Martin J. Best
Geosci. Model Dev., 16, 1875–1886, https://doi.org/10.5194/gmd-16-1875-2023, https://doi.org/10.5194/gmd-16-1875-2023, 2023
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The Joint UK Land Environment Simulator (JULES) uses a tiled representation of land cover but can only model a single dominant soil type within a grid box; hence there is no representation of sub-grid soil heterogeneity. This paper evaluates a new surface–soil tiling scheme in JULES and demonstrates the impacts of the scheme using several soil tiling approaches. Results show that soil tiling has an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture.
Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, and Lianghai Wu
Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, https://doi.org/10.5194/acp-23-3829-2023, 2023
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We present a global dataset of atmospheric CO2 and CH4, the two most important human-made greenhouse gases, which covers almost 2 decades (2003–2020). It is produced by combining satellite data of CO2 and CH4 with a weather and air composition prediction model, and it has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. This dataset can be used for scientific studies in the field of climate change and the global carbon cycle.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
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This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023, https://doi.org/10.5194/hess-27-861-2023, 2023
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Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
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In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Wei Li, Jie Chen, Lu Li, Yvan J. Orsolini, Yiheng Xiang, Retish Senan, and Patricia de Rosnay
The Cryosphere, 16, 4985–5000, https://doi.org/10.5194/tc-16-4985-2022, https://doi.org/10.5194/tc-16-4985-2022, 2022
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Snow assimilation over the Tibetan Plateau (TP) may influence seasonal forecasts over this region. To investigate the impacts of snow assimilation on the seasonal forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer in 2018. The results show that snow assimilation can improve seasonal forecasts over the TP through the interaction between land and atmosphere.
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yaoming Ma, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 14, 5513–5542, https://doi.org/10.5194/essd-14-5513-2022, https://doi.org/10.5194/essd-14-5513-2022, 2022
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Soil moisture and soil temperature (SMST) are important state variables for quantifying the heat–water exchange between land and atmosphere. Yet, long-term, regional-scale in situ SMST measurements at multiple depths are scarce on the Tibetan Plateau (TP). The presented dataset would be valuable for the evaluation and improvement of long-term satellite- and model-based SMST products on the TP, enhancing the understanding of TP hydrometeorological processes and their response to climate change.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
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Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo
Hydrol. Earth Syst. Sci., 26, 5493–5513, https://doi.org/10.5194/hess-26-5493-2022, https://doi.org/10.5194/hess-26-5493-2022, 2022
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When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
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Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Hong Zhao, Yijian Zeng, Jan G. Hofste, Ting Duan, Jun Wen, and Zhongbo Su
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-333, https://doi.org/10.5194/hess-2022-333, 2022
Revised manuscript not accepted
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This paper demonstrated the capability of our developed platform for simulating microwave emission and backscatter signals at multi-frequency. The results of associated investigations on impacts of vegetation water (VW) and temperature (T) imply the need to first disentangle the impact of T for the use of high-frequency signals as its variation is more due to dynamic T. Estimated vegetation optical depth is frequency-dependent, while its diurnal variation depends on that of VW despite frequency.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
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We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
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Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
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.
Javier de la Casa, Adrià Barbeta, Asun Rodríguez-Uña, Lisa Wingate, Jérôme Ogée, and Teresa E. Gimeno
Hydrol. Earth Syst. Sci., 26, 4125–4146, https://doi.org/10.5194/hess-26-4125-2022, https://doi.org/10.5194/hess-26-4125-2022, 2022
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Recently, studies have been reporting mismatches in the water isotopic composition of plants and soils. In this work, we reviewed worldwide isotopic composition data of field and laboratory studies to see if the mismatch is generalised, and we found it to be true. This contradicts theoretical expectations and may underlie an non-described phenomenon that should be forward investigated and implemented in ecohydrological models to avoid erroneous estimations of water sources used by vegetation.
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.
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, https://doi.org/10.5194/hess-26-4013-2022, 2022
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Early flood warnings are one of the most effective tools to save lives and goods. Machine learning (ML) models can improve flood prediction accuracy but their use in operational frameworks is limited. The paper presents a flood warning system, operational in India and Bangladesh, that uses ML models for forecasting river stage and flood inundation maps and discusses the models' performances. In 2021, more than 100 million flood alerts were sent to people near rivers over an area of 470 000 km2.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 22, 8897–8934, https://doi.org/10.5194/acp-22-8897-2022, https://doi.org/10.5194/acp-22-8897-2022, 2022
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We study the interannual variability in Australian carbon fluxes for 2015–2019 derived from OCO-2 satellite data. Our results suggest that Australia's semi-arid ecosystems are highly responsive to variations in climate drivers such as rainfall and temperature. We found that high rainfall and low temperatures recorded in 2016 led to an anomalous carbon sink over savanna and sparsely vegetated regions, while unprecedented dry and hot weather in 2019 led to anomalous carbon release.
Bin Cao, Gabriele Arduini, and Ervin Zsoter
The Cryosphere, 16, 2701–2708, https://doi.org/10.5194/tc-16-2701-2022, https://doi.org/10.5194/tc-16-2701-2022, 2022
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We implemented a new multi-layer snow scheme in the land surface scheme of ERA5-Land with revised snow densification parameterizations. The revised HTESSEL improved the representation of soil temperature in permafrost regions compared to ERA5-Land; in particular, warm bias in winter was significantly reduced, and the resulting modeled near-surface permafrost extent was improved.
Jonathan J. Day, Sarah Keeley, Gabriele Arduini, Linus Magnusson, Kristian Mogensen, Mark Rodwell, Irina Sandu, and Steffen Tietsche
Weather Clim. Dynam., 3, 713–731, https://doi.org/10.5194/wcd-3-713-2022, https://doi.org/10.5194/wcd-3-713-2022, 2022
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A recent drive to develop seamless forecasting systems has culminated in the development of weather forecasting systems that include a coupled representation of the atmosphere, ocean and sea ice. Before this, sea ice and sea surface temperature anomalies were typically fixed throughout a given forecast. We show that the dynamic coupling is most beneficial during periods of rapid ice advance, where persistence is a poor forecast of the sea ice and leads to large errors in the uncoupled system.
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, https://doi.org/10.5194/hess-26-3377-2022, 2022
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The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
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The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
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Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Camille Abadie, Fabienne Maignan, Marine Remaud, Jérôme Ogée, J. Elliott Campbell, Mary E. Whelan, Florian Kitz, Felix M. Spielmann, Georg Wohlfahrt, Richard Wehr, Wu Sun, Nina Raoult, Ulli Seibt, Didier Hauglustaine, Sinikka T. Lennartz, Sauveur Belviso, David Montagne, and Philippe Peylin
Biogeosciences, 19, 2427–2463, https://doi.org/10.5194/bg-19-2427-2022, https://doi.org/10.5194/bg-19-2427-2022, 2022
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A better constraint of the components of the carbonyl sulfide (COS) global budget is needed to exploit its potential as a proxy of gross primary productivity. In this study, we compare two representations of oxic soil COS fluxes, and we develop an approach to represent anoxic soil COS fluxes in a land surface model. We show the importance of atmospheric COS concentration variations on oxic soil COS fluxes and provide new estimates for oxic and anoxic soil contributions to the COS global budget.
Noah D. Smith, Eleanor J. Burke, Kjetil Schanke Aas, Inge H. J. Althuizen, Julia Boike, Casper Tai Christiansen, Bernd Etzelmüller, Thomas Friborg, Hanna Lee, Heather Rumbold, Rachael H. Turton, Sebastian Westermann, and Sarah E. Chadburn
Geosci. Model Dev., 15, 3603–3639, https://doi.org/10.5194/gmd-15-3603-2022, https://doi.org/10.5194/gmd-15-3603-2022, 2022
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The Arctic has large areas of small mounds that are caused by ice lifting up the soil. Snow blown by wind gathers in hollows next to these mounds, insulating them in winter. The hollows tend to be wetter, and thus the soil absorbs more heat in summer. The warm wet soil in the hollows decomposes, releasing methane. We have made a model of this, and we have tested how it behaves and whether it looks like sites in Scandinavia and Siberia. Sometimes we get more methane than a model without mounds.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jianyong Ma, Sam S. Rabin, Peter Anthoni, Anita D. Bayer, Sylvia S. Nyawira, Stefan Olin, Longlong Xia, and Almut Arneth
Biogeosciences, 19, 2145–2169, https://doi.org/10.5194/bg-19-2145-2022, https://doi.org/10.5194/bg-19-2145-2022, 2022
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Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. We simulated the impacts of seven management practices on soil carbon pools, nitrogen loss, and crop yield under different climate scenarios in this region. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates.
Irina Melnikova, Olivier Boucher, Patricia Cadule, Katsumasa Tanaka, Thomas Gasser, Tomohiro Hajima, Yann Quilcaille, Hideo Shiogama, Roland Séférian, Kaoru Tachiiri, Nicolas Vuichard, Tokuta Yokohata, and Philippe Ciais
Earth Syst. Dynam., 13, 779–794, https://doi.org/10.5194/esd-13-779-2022, https://doi.org/10.5194/esd-13-779-2022, 2022
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The deployment of bioenergy crops for capturing carbon from the atmosphere facilitates global warming mitigation via generating negative CO2 emissions. Here, we explored the consequences of large-scale energy crops deployment on the land carbon cycle. The land-use change for energy crops leads to carbon emissions and loss of future potential increase in carbon uptake by natural ecosystems. This impact should be taken into account by the modeling teams and accounted for in mitigation policies.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
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A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
Biogeosciences, 19, 1913–1932, https://doi.org/10.5194/bg-19-1913-2022, https://doi.org/10.5194/bg-19-1913-2022, 2022
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Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that do not occur instantly. In this study, we explore the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing
Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, https://doi.org/10.5194/hess-26-1673-2022, 2022
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This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.
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.
Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Jianyong Ma, Stefan Olin, Peter Anthoni, Sam S. Rabin, Anita D. Bayer, Sylvia S. Nyawira, and Almut Arneth
Geosci. Model Dev., 15, 815–839, https://doi.org/10.5194/gmd-15-815-2022, https://doi.org/10.5194/gmd-15-815-2022, 2022
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The implementation of the biological N fixation process in LPJ-GUESS in this study provides an opportunity to quantify N fixation rates between legumes and to better estimate grain legume production on a global scale. It also helps to predict and detect the potential contribution of N-fixing plants as
green manureto reducing or removing the use of N fertilizer in global agricultural systems, considering different climate conditions, management practices, and land-use change scenarios.
Sami W. Rifai, Martin G. De Kauwe, Anna M. Ukkola, Lucas A. Cernusak, Patrick Meir, Belinda E. Medlyn, and Andy J. Pitman
Biogeosciences, 19, 491–515, https://doi.org/10.5194/bg-19-491-2022, https://doi.org/10.5194/bg-19-491-2022, 2022
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Australia's woody ecosystems have experienced widespread greening despite a warming climate and repeated record-breaking droughts and heat waves. Increasing atmospheric CO2 increases plant water use efficiency, yet quantifying the CO2 effect is complicated due to co-occurring effects of global change. Here we harmonized a 38-year satellite record to separate the effects of climate change, land use change, and disturbance to quantify the CO2 fertilization effect on the greening phenomenon.
Shaoning Lv, Clemens Simmer, Yijian Zeng, Jun Wen, Yuanyuan Guo, and Zhongbo Su
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-369, https://doi.org/10.5194/tc-2021-369, 2022
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The freeze-thaw of the ground is an interesting topic to climatology, hydrology, and other earth sciences. The global freeze-thaw distribution is available by passive microwave remote sensing technique. However, the remote sensing technique indirectly detects freeze-thaw states by measuring the brightness temperature difference between frozen and unfrozen soil. Thus, we present different interprets of the brightness signals to the FT-state by using its sub-daily character.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 21, 17453–17494, https://doi.org/10.5194/acp-21-17453-2021, https://doi.org/10.5194/acp-21-17453-2021, 2021
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Semi-arid ecosystems such as those in Australia are evolving and might play an essential role in the future of climate change. We use carbon dioxide concentrations derived from the OCO-2 satellite instrument and a regional transport model to understand if Australia was a carbon sink or source of CO2 in 2015. Our research's main findings suggest that Australia acted as a carbon sink of about −0.41 ± 0.08 petagrams of carbon in 2015, driven primarily by savanna and sparsely vegetated ecosystems.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Geosci. Model Dev., 14, 7345–7376, https://doi.org/10.5194/gmd-14-7345-2021, https://doi.org/10.5194/gmd-14-7345-2021, 2021
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We developed an integrated soil–snow–atmosphere model (STEMMUS-UEB) dedicated to the physical description of snow and soil processes with various complexities. With STEMMUS-UEB, we demonstrated that the snowpack affects not only the soil surface moisture conditions (in the liquid and ice phase) and energy-related states (albedo, LE) but also the subsurface soil water and vapor transfer, which contributes to a better understanding of the hydrothermal implications of the snowpack in cold regions.
Seoung Soo Lee, Kyung-Ja Ha, Manguttathil Gopalakrishnan Manoj, Mohammad Kamruzzaman, Hyungjun Kim, Nobuyuki Utsumi, Youtong Zheng, Byung-Gon Kim, Chang Hoon Jung, Junshik Um, Jianping Guo, Kyoung Ock Choi, and Go-Un Kim
Atmos. Chem. Phys., 21, 16843–16868, https://doi.org/10.5194/acp-21-16843-2021, https://doi.org/10.5194/acp-21-16843-2021, 2021
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Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Julia Bres, Pierre Sepulchre, Nicolas Viovy, and Nicolas Vuichard
Biogeosciences, 18, 5729–5750, https://doi.org/10.5194/bg-18-5729-2021, https://doi.org/10.5194/bg-18-5729-2021, 2021
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We emulate angiosperm paleo-traits in a land surface model according to the fossil record, and we assess this paleovegetation functioning under different pCO2 from the leaf scale to the global scale. We show that photosynthesis, transpiration and water-use efficiency are dependent on both the vegetation parameterization and the pCO2. Comparing the modeled vegetation with the fossil record, we provide clues on how to account for angiosperm evolutionary traits in paleoclimate simulations.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
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The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Mengna Li, Yijian Zeng, Maciek W. Lubczynski, Jean Roy, Lianyu Yu, Hui Qian, Zhenyu Li, Jie Chen, Lei Han, Han Zheng, Tom Veldkamp, Jeroen M. Schoorl, Harrie-Jan Hendricks Franssen, Kai Hou, Qiying Zhang, Panpan Xu, Fan Li, Kai Lu, Yulin Li, and Zhongbo Su
Earth Syst. Sci. Data, 13, 4727–4757, https://doi.org/10.5194/essd-13-4727-2021, https://doi.org/10.5194/essd-13-4727-2021, 2021
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The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. Borehole core lithology analysis, an altitude survey, soil thickness measurement, hydrogeological surveys, and hydrogeophysical surveys were conducted in the Maqu catchment within the Yellow River source region to improve a full–picture understanding of the water cycle.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
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Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Jina Jeong, Jonathan Barichivich, Philippe Peylin, Vanessa Haverd, Matthew Joseph McGrath, Nicolas Vuichard, Michael Neil Evans, Flurin Babst, and Sebastiaan Luyssaert
Geosci. Model Dev., 14, 5891–5913, https://doi.org/10.5194/gmd-14-5891-2021, https://doi.org/10.5194/gmd-14-5891-2021, 2021
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We have proposed and evaluated the use of four benchmarks that leverage tree-ring width observations to provide more nuanced verification targets for land-surface models (LSMs), which currently lack a long-term benchmark for forest ecosystem functioning. Using relatively unbiased European biomass network datasets, we identify the extent to which presumed biases in the much larger International Tree-Ring Data Bank might degrade the validation of LSMs.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
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We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
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Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
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The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Hong-Yu Xie, Xiao-Wei Jiang, Shu-Cong Tan, Li Wan, Xu-Sheng Wang, Si-Hai Liang, and Yijian Zeng
Hydrol. Earth Syst. Sci., 25, 4243–4257, https://doi.org/10.5194/hess-25-4243-2021, https://doi.org/10.5194/hess-25-4243-2021, 2021
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Freezing-induced groundwater migration and water table decline are widely observed, but quantitative understanding of these processes is lacking. By considering wintertime atmospheric conditions and occurrence of lateral groundwater inflow, a model coupling soil water and groundwater reproduced field observations of soil temperature, soil water content, and groundwater level well. The model results led to a clear understanding of the balance of the water budget during the freezing–thawing cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
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Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Sian Kou-Giesbrecht, Sergey Malyshev, Isabel Martínez Cano, Stephen W. Pacala, Elena Shevliakova, Thomas A. Bytnerowicz, and Duncan N. L. Menge
Biogeosciences, 18, 4143–4183, https://doi.org/10.5194/bg-18-4143-2021, https://doi.org/10.5194/bg-18-4143-2021, 2021
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Representing biological nitrogen fixation (BNF) is an important challenge for land models. We present a novel representation of BNF and updated nitrogen cycling in a land model. It includes a representation of asymbiotic BNF by soil microbes and the competitive dynamics between nitrogen-fixing and non-fixing plants. It improves estimations of major carbon and nitrogen pools and fluxes and their temporal dynamics in comparison to previous representations of BNF in land models.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Pei Zhang, Donghai Zheng, Rogier van der Velde, Jun Wen, Yijian Zeng, Xin Wang, Zuoliang Wang, Jiali Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3075–3102, https://doi.org/10.5194/essd-13-3075-2021, https://doi.org/10.5194/essd-13-3075-2021, 2021
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This paper reports on the status of the Tibet-Obs and presents a 10-year (2009–2019) surface soil moisture (SM) dataset produced based on in situ measurements taken at a depth of 5 cm collected from the Tibet-Obs. This surface SM dataset includes the original 15 min in situ measurements collected by multiple SM monitoring sites of three networks (i.e. the Maqu, Naqu, and Ngari networks) and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
Jonathan Barichivich, Philippe Peylin, Thomas Launois, Valerie Daux, Camille Risi, Jina Jeong, and Sebastiaan Luyssaert
Biogeosciences, 18, 3781–3803, https://doi.org/10.5194/bg-18-3781-2021, https://doi.org/10.5194/bg-18-3781-2021, 2021
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The width and the chemical signals of tree rings have the potential to test and improve the physiological responses simulated by global land surface models, which are at the core of future climate projections. Here, we demonstrate the novel use of tree-ring width and carbon and oxygen stable isotopes to evaluate the representation of tree growth and physiology in a global land surface model at temporal scales beyond experimentation and direct observation.
Jan G. Hofste, Rogier van der Velde, Jun Wen, Xin Wang, Zuoliang Wang, Donghai Zheng, Christiaan van der Tol, and Zhongbo Su
Earth Syst. Sci. Data, 13, 2819–2856, https://doi.org/10.5194/essd-13-2819-2021, https://doi.org/10.5194/essd-13-2819-2021, 2021
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The dataset reported in this paper concerns the measurement of microwave reflections from an alpine meadow over the Tibetan Plateau. These microwave reflections were measured continuously over 1 year. With it, variations in soil water content due to evaporation, precipitation, drainage, and soil freezing/thawing can be seen. A better understanding of the effects aforementioned processes have on microwave reflections may improve methods for estimating soil water content used by satellites.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Sam P. Jones, Aurore Kaisermann, Jérôme Ogée, Steven Wohl, Alexander W. Cheesman, Lucas A. Cernusak, and Lisa Wingate
SOIL, 7, 145–159, https://doi.org/10.5194/soil-7-145-2021, https://doi.org/10.5194/soil-7-145-2021, 2021
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Understanding how the rate of oxygen isotope exchange between water and CO2 varies in soils is key for using the oxygen isotope composition of atmospheric CO2 as a tracer of biosphere CO2 fluxes at large scales. Across 44 diverse soils the rate of this exchange responded to pH, nitrate and microbial biomass, which are hypothesised to alter activity of the enzyme carbonic anhydrase in soils. Using these three soil traits, it is now possible to predict how this isotopic exchange varies spatially.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
Wolfgang A. Obermeier, Julia E. M. S. Nabel, Tammas Loughran, Kerstin Hartung, Ana Bastos, Felix Havermann, Peter Anthoni, Almut Arneth, Daniel S. Goll, Sebastian Lienert, Danica Lombardozzi, Sebastiaan Luyssaert, Patrick C. McGuire, Joe R. Melton, Benjamin Poulter, Stephen Sitch, Michael O. Sullivan, Hanqin Tian, Anthony P. Walker, Andrew J. Wiltshire, Soenke Zaehle, and Julia Pongratz
Earth Syst. Dynam., 12, 635–670, https://doi.org/10.5194/esd-12-635-2021, https://doi.org/10.5194/esd-12-635-2021, 2021
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We provide the first spatio-temporally explicit comparison of different model-derived fluxes from land use and land cover changes (fLULCCs) by using the TRENDY v8 dynamic global vegetation models used in the 2019 global carbon budget. We find huge regional fLULCC differences resulting from environmental assumptions, simulated periods, and the timing of land use and land cover changes, and we argue for a method consistent across time and space and for carefully choosing the accounting period.
Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, https://doi.org/10.5194/hess-25-2685-2021, 2021
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We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.
Fabienne Maignan, Camille Abadie, Marine Remaud, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Róisín Commane, Richard Wehr, J. Elliott Campbell, Sauveur Belviso, Stephen A. Montzka, Nina Raoult, Ulli Seibt, Yoichi P. Shiga, Nicolas Vuichard, Mary E. Whelan, and Philippe Peylin
Biogeosciences, 18, 2917–2955, https://doi.org/10.5194/bg-18-2917-2021, https://doi.org/10.5194/bg-18-2917-2021, 2021
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The assimilation of carbonyl sulfide (COS) by continental vegetation has been proposed as a proxy for gross primary production (GPP). Using a land surface and a transport model, we compare a mechanistic representation of the plant COS uptake (Berry et al., 2013) to the classical leaf relative uptake (LRU) approach linking GPP and vegetation COS fluxes. We show that at high temporal resolutions a mechanistic approach is mandatory, but at large scales the LRU approach compares similarly.
Hiroki Mizuochi, Agnès Ducharne, Frédérique Cheruy, Josefine Ghattas, Amen Al-Yaari, Jean-Pierre Wigneron, Vladislav Bastrikov, Philippe Peylin, Fabienne Maignan, and Nicolas Vuichard
Hydrol. Earth Syst. Sci., 25, 2199–2221, https://doi.org/10.5194/hess-25-2199-2021, https://doi.org/10.5194/hess-25-2199-2021, 2021
Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Nearing, Jimmy Lin, and Sepp Hochreiter
Hydrol. Earth Syst. Sci., 25, 2045–2062, https://doi.org/10.5194/hess-25-2045-2021, https://doi.org/10.5194/hess-25-2045-2021, 2021
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We present multi-timescale Short-Term Memory (MTS-LSTM), a machine learning approach that predicts discharge at multiple timescales within one model. MTS-LSTM is significantly more accurate than the US National Water Model and computationally more efficient than an individual LSTM model per timescale. Further, MTS-LSTM can process different input variables at different timescales, which is important as the lead time of meteorological forecasts often depends on their temporal resolution.
Daniele Peano, Deborah Hemming, Stefano Materia, Christine Delire, Yuanchao Fan, Emilie Joetzjer, Hanna Lee, Julia E. M. S. Nabel, Taejin Park, Philippe Peylin, David Wårlind, Andy Wiltshire, and Sönke Zaehle
Biogeosciences, 18, 2405–2428, https://doi.org/10.5194/bg-18-2405-2021, https://doi.org/10.5194/bg-18-2405-2021, 2021
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Global climate models are the scientist’s tools used for studying past, present, and future climate conditions. This work examines the ability of a group of our tools in reproducing and capturing the right timing and length of the season when plants show their green leaves. This season, indeed, is fundamental for CO2 exchanges between land, atmosphere, and climate. This work shows that discrepancies compared to observations remain, demanding further polishing of these tools.
Anita D. Bayer, Richard Fuchs, Reinhard Mey, Andreas Krause, Peter H. Verburg, Peter Anthoni, and Almut Arneth
Earth Syst. Dynam., 12, 327–351, https://doi.org/10.5194/esd-12-327-2021, https://doi.org/10.5194/esd-12-327-2021, 2021
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Many projections of future land-use/-cover exist. We evaluate a number of these and determine the variability they cause in ecosystems and their services. We found that projections differ a lot in regional patterns, with some patterns being at least questionable in a historical context. Across ecosystem service indicators, resulting variability until 2040 was highest in crop production. Results emphasize that such variability should be acknowledged in assessments of future ecosystem provisions.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203, https://doi.org/10.5194/bg-18-2181-2021, https://doi.org/10.5194/bg-18-2181-2021, 2021
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The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van der Tol, Qiang Yu, Xiaoliang Lü, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev., 14, 1379–1407, https://doi.org/10.5194/gmd-14-1379-2021, https://doi.org/10.5194/gmd-14-1379-2021, 2021
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This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved by considering the root water uptake, especially when vegetation was experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer in simulating ecosystem functioning.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
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We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
María P. González-Dugo, Xuelong Chen, Ana Andreu, Elisabet Carpintero, Pedro J. Gómez-Giraldez, Arnaud Carrara, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, https://doi.org/10.5194/hess-25-755-2021, 2021
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Drought is a devastating natural hazard and difficult to define, detect and quantify. Global meteorological data and remote-sensing products present new opportunities to characterize drought in an objective way. In this paper, we applied the surface energy balance model SEBS to estimate monthly evapotranspiration (ET) from 2001 to 2018 over the dehesa area of the Iberian Peninsula. ET anomalies were used to identify the main drought events and analyze their impacts on dehesa vegetation.
Chihiro Kodama, Tomoki Ohno, Tatsuya Seiki, Hisashi Yashiro, Akira T. Noda, Masuo Nakano, Yohei Yamada, Woosub Roh, Masaki Satoh, Tomoko Nitta, Daisuke Goto, Hiroaki Miura, Tomoe Nasuno, Tomoki Miyakawa, Ying-Wen Chen, and Masato Sugi
Geosci. Model Dev., 14, 795–820, https://doi.org/10.5194/gmd-14-795-2021, https://doi.org/10.5194/gmd-14-795-2021, 2021
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This paper describes the latest stable version of NICAM, a global atmospheric model, developed for high-resolution climate simulations toward the IPCC Assessment Report. Our model explicitly treats convection, clouds, and precipitation and could reduce the uncertainty of climate change projection. A series of test simulations demonstrated improvements (e.g., high cloud) and issues (e.g., low cloud, precipitation pattern), suggesting further necessity for model improvement and higher resolutions.
Rogier van der Velde, Andreas Colliander, Michiel Pezij, Harm-Jan F. Benninga, Rajat Bindlish, Steven K. Chan, Thomas J. Jackson, Dimmie M. D. Hendriks, Denie C. M. Augustijn, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 473–495, https://doi.org/10.5194/hess-25-473-2021, https://doi.org/10.5194/hess-25-473-2021, 2021
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NASA’s SMAP satellite provides estimates of the amount of water in the soil. With measurements from a network of 20 monitoring stations, the accuracy of these estimates has been studied for a 4-year period. We found an agreement between satellite and in situ estimates in line with the mission requirements once the large mismatches associated with rapidly changing water contents, e.g. soil freezing and rainfall, are excluded.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Teresa E. Gimeno, Belinda E. Medlyn, Dani Or, Jinyan Yang, and David S. Ellsworth
Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, https://doi.org/10.5194/hess-25-447-2021, 2021
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Land surface model (LSM) is a critical tool to study land responses to droughts and heatwaves, but lacking comprehensive observations limited past model evaluations. Here we use a novel dataset at a water-limited site, evaluate a typical LSM with a range of competing model hypotheses widely used in LSMs and identify marked uncertainty due to the differing process assumptions. We show the extensive observations constrain model processes and allow better simulated land responses to these extremes.
Jan Pisek, Angela Erb, Lauri Korhonen, Tobias Biermann, Arnaud Carrara, Edoardo Cremonese, Matthias Cuntz, Silvano Fares, Giacomo Gerosa, Thomas Grünwald, Niklas Hase, Michal Heliasz, Andreas Ibrom, Alexander Knohl, Johannes Kobler, Bart Kruijt, Holger Lange, Leena Leppänen, Jean-Marc Limousin, Francisco Ramon Lopez Serrano, Denis Loustau, Petr Lukeš, Lars Lundin, Riccardo Marzuoli, Meelis Mölder, Leonardo Montagnani, Johan Neirynck, Matthias Peichl, Corinna Rebmann, Eva Rubio, Margarida Santos-Reis, Crystal Schaaf, Marius Schmidt, Guillaume Simioni, Kamel Soudani, and Caroline Vincke
Biogeosciences, 18, 621–635, https://doi.org/10.5194/bg-18-621-2021, https://doi.org/10.5194/bg-18-621-2021, 2021
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Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
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Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
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The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
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The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
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We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
Natasha MacBean, Russell L. Scott, Joel A. Biederman, Catherine Ottlé, Nicolas Vuichard, Agnès Ducharne, Thomas Kolb, Sabina Dore, Marcy Litvak, and David J. P. Moore
Hydrol. Earth Syst. Sci., 24, 5203–5230, https://doi.org/10.5194/hess-24-5203-2020, https://doi.org/10.5194/hess-24-5203-2020, 2020
George C. Hurtt, Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Justin Fisk, Shinichiro Fujimori, Kees Klein Goldewijk, Tomoko Hasegawa, Peter Havlik, Andreas Heinimann, Florian Humpenöder, Johan Jungclaus, Jed O. Kaplan, Jennifer Kennedy, Tamás Krisztin, David Lawrence, Peter Lawrence, Lei Ma, Ole Mertz, Julia Pongratz, Alexander Popp, Benjamin Poulter, Keywan Riahi, Elena Shevliakova, Elke Stehfest, Peter Thornton, Francesco N. Tubiello, Detlef P. van Vuuren, and Xin Zhang
Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, https://doi.org/10.5194/gmd-13-5425-2020, 2020
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To estimate the effects of human land use activities on the carbon–climate system, a new set of global gridded land use forcing datasets was developed to link historical land use data to eight future scenarios in a standard format required by climate models. This new generation of land use harmonization (LUH2) includes updated inputs, higher spatial resolution, more detailed land use transitions, and the addition of important agricultural management layers; it will be used for CMIP6 simulations.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
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We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Xu Yuan, Xiaolong Yu, and Zhongbo Su
Ocean Sci., 16, 1285–1296, https://doi.org/10.5194/os-16-1285-2020, https://doi.org/10.5194/os-16-1285-2020, 2020
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This work investigates the variabilities of the barrier layer thickness (BLT) in the tropical Indian Ocean with the Simple Ocean Data Assimilation version 3 ocean reanalysis data. Our results show that the seasonal variation of the BLT is in relation to the changes of thermocline and sea surface salinity. In terms of the interannual timescale, BLT presents a clear seasonal phase locking dominated by different drivers during the Indian Dipole and El Niño–Southern Oscillation events.
Guillaume Monteil, Grégoire Broquet, Marko Scholze, Matthew Lang, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Naomi E. Smith, Rona L. Thompson, Ingrid T. Luijkx, Emily White, Antoon Meesters, Philippe Ciais, Anita L. Ganesan, Alistair Manning, Michael Mischurow, Wouter Peters, Philippe Peylin, Jerôme Tarniewicz, Matt Rigby, Christian Rödenbeck, Alex Vermeulen, and Evie M. Walton
Atmos. Chem. Phys., 20, 12063–12091, https://doi.org/10.5194/acp-20-12063-2020, https://doi.org/10.5194/acp-20-12063-2020, 2020
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The paper presents the first results from the EUROCOM project, a regional atmospheric inversion intercomparison exercise involving six European research groups. It aims to produce an estimate of the net carbon flux between the European terrestrial ecosystems and the atmosphere for the period 2006–2015, based on constraints provided by observed CO2 concentrations and using inverse modelling techniques. The use of six different models enables us to investigate the robustness of the results.
Lianyu Yu, Yijian Zeng, and Zhongbo Su
Hydrol. Earth Syst. Sci., 24, 4813–4830, https://doi.org/10.5194/hess-24-4813-2020, https://doi.org/10.5194/hess-24-4813-2020, 2020
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Soil mass and heat transfer processes were represented in three levels of model complexities to understand soil freeze–thaw mechanisms. Results indicate that coupled mass and heat transfer models considerably improved simulations of the soil hydrothermal regime. Vapor flow and thermal effects on water flow are the main mechanisms for the improvements. Given the explicit consideration of airflow, vapor flow and its effects on heat transfer were enhanced during the freeze–thaw transition period.
Tokuta Yokohata, Tsuguki Kinoshita, Gen Sakurai, Yadu Pokhrel, Akihiko Ito, Masashi Okada, Yusuke Satoh, Etsushi Kato, Tomoko Nitta, Shinichiro Fujimori, Farshid Felfelani, Yoshimitsu Masaki, Toshichika Iizumi, Motoki Nishimori, Naota Hanasaki, Kiyoshi Takahashi, Yoshiki Yamagata, and Seita Emori
Geosci. Model Dev., 13, 4713–4747, https://doi.org/10.5194/gmd-13-4713-2020, https://doi.org/10.5194/gmd-13-4713-2020, 2020
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The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balances, human water management, and crop growth incorporates a land-use decision-making model based on economic activities. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand.
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Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land...
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