Articles | Volume 8, issue 2
https://doi.org/10.5194/gmd-8-431-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-431-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
24 Feb 2015
Development and technical paper |
| 24 Feb 2015
A test of an optimal stomatal conductance scheme within the CABLE land surface model
Macquarie University, Sydney, Australia
J. Kala
Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
Y.-S. Lin
Macquarie University, Sydney, Australia
A. J. Pitman
Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
B. E. Medlyn
Macquarie University, Sydney, Australia
R. A. Duursma
Hawkesbury Institute for the Environment, University of Western Sydney, Sydney, Australia
G. Abramowitz
Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
Y.-P. Wang
CSIRO Ocean and Atmosphere Flagship, Private Bag #1, Aspendale, Victoria 3195, Australia
D. G. Miralles
Department of Earth Sciences, VU University, Amsterdam 1081 HV, the Netherlands
Laboratory of Hydrology and Water Management, Ghent University, 9000 Ghent, Belgium
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Anna M. Ukkola, Andy J. Pitman, Mark Decker, Martin G. De Kauwe, Gab Abramowitz, Jatin Kala, and Ying-Ping Wang
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M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, https://doi.org/10.5194/bg-12-7503-2015, 2015
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J. Kala, M. G. De Kauwe, A. J. Pitman, R. Lorenz, B. E. Medlyn, Y.-P Wang, Y.-S Lin, and G. Abramowitz
Geosci. Model Dev., 8, 3877–3889, https://doi.org/10.5194/gmd-8-3877-2015, https://doi.org/10.5194/gmd-8-3877-2015, 2015
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Oscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Diego G. Miralles, Hylke E. Beck, Jonatan F. Siegmund, Camila Alvarez-Garreton, Koen Verbist, René Garreaud, Juan Pablo Boisier, and Mauricio Galleguillos
Hydrol. Earth Syst. Sci., 28, 1415–1439, https://doi.org/10.5194/hess-28-1415-2024, https://doi.org/10.5194/hess-28-1415-2024, 2024
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Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
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We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-114, https://doi.org/10.5194/gmd-2023-114, 2023
Revised manuscript accepted for GMD
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Because the impact of biochar on SOC dynamics is not represented in most land carbon models used for assessing land-based climate mitigation, our study develops a biochar model that incorporates our current understanding of biochar effects on SOC based on a soil carbon model (MIMICS) for future predictions at the regional and global scale. The MIMICS model was improved with new processes considered and the developed biochar model can generally reproduce the SOC changes after adding biochar.
Steven J. De Hertog, Carmen E. Lopez-Fabara, Ruud van der Ent, Jessica Keune, Diego G. Miralles, Raphael Portmann, Sebastian Schemm, Felix Havermann, Suqi Guo, Fei Luo, Iris Manola, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2023-953, https://doi.org/10.5194/egusphere-2023-953, 2023
Preprint archived
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Land cover and management changes can affect the climate and water availability. In this study we use climate model simulations of extreme global land cover changes (afforestation, deforestation) and land management changes (irrigation) to understand the effects on the global water cycle and local to continental water availability. We show that cropland expansion generally leads to higher evaporation and lower amounts of precipitation and afforestation and irrigation expansion to the opposite.
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.
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
Short summary
Short summary
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.
Feng Zhong, Shanhu Jiang, Albert I. J. M. van Dijk, Liliang Ren, Jaap Schellekens, and Diego G. Miralles
Hydrol. Earth Syst. Sci., 26, 5647–5667, https://doi.org/10.5194/hess-26-5647-2022, https://doi.org/10.5194/hess-26-5647-2022, 2022
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A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1° spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
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.
Jessica Keune, Dominik L. Schumacher, and Diego G. Miralles
Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, https://doi.org/10.5194/gmd-15-1875-2022, 2022
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Air transports moisture and heat, shaping the weather we experience. When and where was this air moistened and warmed by the surface? To address this question, atmospheric models trace the history of air parcels in space and time. However, their uncertainties remain unexplored, which hinders their utility and application. Here, we present a framework that sheds light on these uncertainties. Our approach sets a new standard in the assessment of atmospheric moisture and heat trajectories.
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.
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.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
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Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
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.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
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We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
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.
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
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.
Christopher Krich, Mirco Migliavacca, Diego G. Miralles, Guido Kraemer, Tarek S. El-Madany, Markus Reichstein, Jakob Runge, and Miguel D. Mahecha
Biogeosciences, 18, 2379–2404, https://doi.org/10.5194/bg-18-2379-2021, https://doi.org/10.5194/bg-18-2379-2021, 2021
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Ecosystems and the atmosphere interact with each other. These interactions determine e.g. the water and carbon fluxes and thus are crucial to understand climate change effects. We analysed the interactions for many ecosystems across the globe, showing that very different ecosystems can have similar interactions with the atmosphere. Meteorological conditions seem to be the strongest interaction-shaping factor. This means that common principles can be identified to describe ecosystem behaviour.
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.
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.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
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Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Brecht Martens, Dominik L. Schumacher, Hendrik Wouters, Joaquín Muñoz-Sabater, Niko E. C. Verhoest, and Diego G. Miralles
Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020, https://doi.org/10.5194/gmd-13-4159-2020, 2020
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Climate reanalyses are widely used in different fields and an in-depth evaluation of the different variables provided by reanalyses is a necessary means to provide feedback on the quality to their users and the operational centres producing these data sets. In this study, we show the improvements of ECMWF's latest climate reanalysis (ERA5) upon its predecessor (ERA-Interim) in partitioning the available energy at the land surface.
Christian Massari, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, and Diego Fernandez Prieto
Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, https://doi.org/10.5194/hess-24-2687-2020, 2020
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Rain gauges are unevenly spaced around the world with extremely low gauge density over places like Africa and South America. Here, water-related problems like floods, drought and famine are particularly severe and able to cause fatalities, migration and diseases. We have developed a rainfall dataset that exploits the synergies between rainfall and soil moisture to provide accurate rainfall observations which can be used to face these problems.
Jian Peng, Simon Dadson, Feyera Hirpa, Ellen Dyer, Thomas Lees, Diego G. Miralles, Sergio M. Vicente-Serrano, and Chris Funk
Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, https://doi.org/10.5194/essd-12-753-2020, 2020
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Africa has been severely influenced by intense drought events, which has led to crop failure, food shortages, famine, epidemics and even mass migration. The current study developed a high spatial resolution drought dataset entirely from satellite-based products. The dataset has been comprehensively inter-compared with other drought indicators and may contribute to an improved characterization of drought risk and vulnerability and minimize drought's impact on water and food security in Africa.
Christopher Krich, Jakob Runge, Diego G. Miralles, Mirco Migliavacca, Oscar Perez-Priego, Tarek El-Madany, Arnaud Carrara, and Miguel D. Mahecha
Biogeosciences, 17, 1033–1061, https://doi.org/10.5194/bg-17-1033-2020, https://doi.org/10.5194/bg-17-1033-2020, 2020
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Causal inference promises new insight into biosphere–atmosphere interactions using time series only. To understand the behaviour of a specific method on such data, we used artificial and observation-based data. The observed structures are very interpretable and reveal certain ecosystem-specific behaviour, as only a few relevant links remain, in contrast to pure correlation techniques. Thus, causal inference allows to us gain well-constrained insights into processes and interactions.
Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo, Lina M. Estupinan-Suarez, Victor H. Gutierrez-Velez, Martin Gutwin, Martin Jung, Maria C. Londoño, Diego G. Miralles, Phillip Papastefanou, and Markus Reichstein
Earth Syst. Dynam., 11, 201–234, https://doi.org/10.5194/esd-11-201-2020, https://doi.org/10.5194/esd-11-201-2020, 2020
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The ever-growing availability of data streams on different subsystems of the Earth brings unprecedented scientific opportunities. However, researching a data-rich world brings novel challenges. We present the concept of
Earth system data cubesto study the complex dynamics of multiple climate and ecosystem variables across space and time. Using a series of example studies, we highlight the potential of effectively considering the full multivariate nature of processes in the Earth system.
Jun Ge, Andrew J. Pitman, Weidong Guo, Beilei Zan, and Congbin Fu
Hydrol. Earth Syst. Sci., 24, 515–533, https://doi.org/10.5194/hess-24-515-2020, https://doi.org/10.5194/hess-24-515-2020, 2020
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We investigate the impact of revegetation on the hydrology of the Loess Plateau based on high-resolution simulations using the Weather Research and Forecasting (WRF) model. We find that past revegetation has caused decreased runoff and soil moisture with increased evapotranspiration as well as little response from rainfall. WRF suggests that further revegetation could aggravate this water imbalance. We caution that further revegetation might be unsustainable in this region.
Jinyan Yang, Belinda E. Medlyn, Martin G. De Kauwe, Remko A. Duursma, Mingkai Jiang, Dushan Kumarathunge, Kristine Y. Crous, Teresa E. Gimeno, Agnieszka Wujeska-Klause, and David S. Ellsworth
Biogeosciences, 17, 265–279, https://doi.org/10.5194/bg-17-265-2020, https://doi.org/10.5194/bg-17-265-2020, 2020
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This study addressed a major knowledge gap in the response of forest productivity to elevated CO2. We first quantified forest productivity of an evergreen forest under both ambient and elevated CO2, using a model constrained by in situ measurements. The simulation showed the canopy productivity response to elevated CO2 to be smaller than that at the leaf scale due to different limiting processes. This finding provides a key reference for the understanding of CO2 impacts on forest ecosystems.
Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019, https://doi.org/10.5194/bg-16-4851-2019, 2019
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Bidirectional interactions between vegetation and climate are unraveled over short (monthly) and long (inter-annual) temporal scales. Analyses use a novel causal inference method based on wavelet theory. The performance of climate models at representing these interactions is benchmarked against satellite data. Climate models can reproduce the overall climate controls on vegetation at all temporal scales, while their performance at representing biophysical feedbacks on climate is less adequate.
Paul C. Stoy, Tarek S. El-Madany, Joshua B. Fisher, Pierre Gentine, Tobias Gerken, Stephen P. Good, Anne Klosterhalfen, Shuguang Liu, Diego G. Miralles, Oscar Perez-Priego, Angela J. Rigden, Todd H. Skaggs, Georg Wohlfahrt, Ray G. Anderson, A. Miriam J. Coenders-Gerrits, Martin Jung, Wouter H. Maes, Ivan Mammarella, Matthias Mauder, Mirco Migliavacca, Jacob A. Nelson, Rafael Poyatos, Markus Reichstein, Russell L. Scott, and Sebastian Wolf
Biogeosciences, 16, 3747–3775, https://doi.org/10.5194/bg-16-3747-2019, https://doi.org/10.5194/bg-16-3747-2019, 2019
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Key findings are the nearly optimal response of T to atmospheric water vapor pressure deficits across methods and scales. Additionally, the notion that T / ET intermittently approaches 1, which is a basis for many partitioning methods, does not hold for certain methods and ecosystems. To better constrain estimates of E and T from combined ET measurements, we propose a combination of independent measurement techniques to better constrain E and T at the ecosystem scale.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences, 16, 3069–3093, https://doi.org/10.5194/bg-16-3069-2019, https://doi.org/10.5194/bg-16-3069-2019, 2019
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This study presents an estimate of global terrestrial photosynthesis. We make use of satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and estimate photosynthetic carbon uptake. This new framework incorporates nonlinear, process-based understanding of the link between fluorescence and photosynthesis, an advance on past approaches. This will aid in the utility of fluorescence to quantify terrestrial carbon cycle feedbacks.
Hendrik Wouters, Irina Y. Petrova, Chiel C. van Heerwaarden, Jordi Vilà-Guerau de Arellano, Adriaan J. Teuling, Vicky Meulenberg, Joseph A. Santanello, and Diego G. Miralles
Geosci. Model Dev., 12, 2139–2153, https://doi.org/10.5194/gmd-12-2139-2019, https://doi.org/10.5194/gmd-12-2139-2019, 2019
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The free software CLASS4GL (http://class4gl.eu) is designed to investigate the dynamic atmospheric boundary layer (ABL) with weather balloons. It mines observational data from global radio soundings, satellite and reanalysis data from the last 40 years to constrain and initialize an ABL model and automizes multiple experiments in parallel. CLASS4GL aims at fostering a better understanding of land–atmosphere feedbacks and the drivers of extreme weather.
Mingkai Jiang, Sönke Zaehle, Martin G. De Kauwe, Anthony P. Walker, Silvia Caldararu, David S. Ellsworth, and Belinda E. Medlyn
Geosci. Model Dev., 12, 2069–2089, https://doi.org/10.5194/gmd-12-2069-2019, https://doi.org/10.5194/gmd-12-2069-2019, 2019
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Here we used a simple analytical framework developed by Comins and McMurtrie (1993) to investigate how different model assumptions affected plant responses to elevated CO2. This framework is useful in revealing both the consequences and the mechanisms through which different assumptions affect predictions. We therefore recommend the use of this framework to analyze the likely outcomes of new assumptions before introducing them to complex model structures.
Sophie V. J. van der Horst, Andrew J. Pitman, Martin G. De Kauwe, Anna Ukkola, Gab Abramowitz, and Peter Isaac
Biogeosciences, 16, 1829–1844, https://doi.org/10.5194/bg-16-1829-2019, https://doi.org/10.5194/bg-16-1829-2019, 2019
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Measurements of surface fluxes are taken around the world and are extremely valuable for understanding how the land and atmopshere interact, and how the land can amplify temerature extremes. However, do these measurements sample extreme temperatures, or are they biased to the average? We examine this question and highlight data that do measure surface fluxes under extreme conditions. This provides a way forward to help model developers improve their models.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
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Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Martin G. De Kauwe, Belinda E. Medlyn, Andrew J. Pitman, John E. Drake, Anna Ukkola, Anne Griebel, Elise Pendall, Suzanne Prober, and Michael Roderick
Biogeosciences, 16, 903–916, https://doi.org/10.5194/bg-16-903-2019, https://doi.org/10.5194/bg-16-903-2019, 2019
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Recent experimental evidence suggests that during heat extremes, trees may reduce photosynthesis to near zero but increase transpiration. Using eddy covariance data and examining the 3 days leading up to a temperature extreme, we found evidence of reduced photosynthesis and sustained or increased latent heat fluxes at Australian wooded flux sites. However, when focusing on heatwaves, we were unable to disentangle photosynthetic decoupling from the effect of increasing vapour pressure deficit.
Wouter H. Maes, Pierre Gentine, Niko E. C. Verhoest, and Diego G. Miralles
Hydrol. Earth Syst. Sci., 23, 925–948, https://doi.org/10.5194/hess-23-925-2019, https://doi.org/10.5194/hess-23-925-2019, 2019
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Potential evaporation (Ep) is the amount of water an ecosystem would consume if it were not limited by water availability or other stress factors. In this study, we compared several methods to estimate Ep using a global dataset of 107 FLUXNET sites. A simple radiation-driven method calibrated per biome consistently outperformed more complex approaches and makes a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.
Gab Abramowitz, Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Robert Pincus, and Gavin A. Schmidt
Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, https://doi.org/10.5194/esd-10-91-2019, 2019
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Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 23, 851–870, https://doi.org/10.5194/hess-23-851-2019, https://doi.org/10.5194/hess-23-851-2019, 2019
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
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Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
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How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
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We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Carlos Jiménez, Brecht Martens, Diego M. Miralles, Joshua B. Fisher, Hylke E. Beck, and Diego Fernández-Prieto
Hydrol. Earth Syst. Sci., 22, 4513–4533, https://doi.org/10.5194/hess-22-4513-2018, https://doi.org/10.5194/hess-22-4513-2018, 2018
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Observing the amount of water evaporated in nature is not easy, and we need to combine accurate local measurements with estimates from satellites, more uncertain but covering larger areas. This is the main topic of our paper, in which local observations are compared with global land evaporation estimates, followed by a weighting of the global observations based on this comparison to attempt derive a more accurate evaporation product.
Anthony P. Walker, Ming Ye, Dan Lu, Martin G. De Kauwe, Lianhong Gu, Belinda E. Medlyn, Alistair Rogers, and Shawn P. Serbin
Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, https://doi.org/10.5194/gmd-11-3159-2018, 2018
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Large uncertainty is inherent in model predictions due to imperfect knowledge of how to describe the processes that a model is intended to represent. Yet methods to quantify and evaluate this model hypothesis uncertainty are limited. To address this, the multi-assumption architecture and testbed (MAAT) automates the generation of all possible models by combining multiple representations of multiple processes. MAAT provides a formal framework for quantification of model hypothesis uncertainty.
Ned Haughton, Gab Abramowitz, Martin G. De Kauwe, and Andy J. Pitman
Biogeosciences, 15, 4495–4513, https://doi.org/10.5194/bg-15-4495-2018, https://doi.org/10.5194/bg-15-4495-2018, 2018
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This project explores predictability in energy, water, and carbon fluxes in the free-use Tier 1 of the FLUXNET 2015 dataset using a uniqueness metric based on comparison of locally and globally trained models. While there is broad spread in predictability between sites, we found strikingly few strong patterns. Nevertheless, these results can contribute to the standardisation of site selection for land surface model evaluation and help pinpoint regions that are ripe for further FLUXNET research.
Kashif Mahmud, Belinda E. Medlyn, Remko A. Duursma, Courtney Campany, and Martin G. De Kauwe
Biogeosciences, 15, 4003–4018, https://doi.org/10.5194/bg-15-4003-2018, https://doi.org/10.5194/bg-15-4003-2018, 2018
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A major limitation of current terrestrial vegetation models is that we do not know how to model C balance processes under sink-limited conditions. To address this limitation, we applied data assimilation of a simple C balance model to a manipulative experiment in which sink limitation was induced with low rooting volume. Our analysis framework allowed us to infer that, in addition to a feedback on photosynthetic rates, the reduction in growth was effected by other C balance processes.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-270, https://doi.org/10.5194/bg-2018-270, 2018
Revised manuscript has not been submitted
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This study presents a global estimate of land carbon uptake through photosynthesis. We make use satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and then use the optimized model to estimate photosynthetic carbon uptake. This provides a new tool that can combine measurements and observations in a systematic way and maximise the use of chlorophyll fluorescence to improve our understanding of the land carbon cycle.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
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Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Anna Ukkola
Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
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We present a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009 and 0.5 grid cell size. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. We confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also show that the weighted product performs better than 10 different existing global ET datasets in a range of metrics.
Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson
Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, https://doi.org/10.5194/esd-9-135-2018, 2018
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Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Wouter H. Maes, Pierre Gentine, Niko E. C. Verhoest, and Diego G. Miralles
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-682, https://doi.org/10.5194/hess-2017-682, 2018
Revised manuscript not accepted
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Potential evaporation is a key parameter in numerous models used for assessing water use and drought severity. Yet, multiple incompatible methods have been proposed, thus estimates of potential evaporation remain uncertain. Based on the largest available dataset of FLUXNET data, we identify the best method to calculate potential evaporation globally. A simple radiation-driven method calibrated per biome consistently performed best; more complex models did not perform as good.
Ned Haughton, Gab Abramowitz, and Andy J. Pitman
Geosci. Model Dev., 11, 195–212, https://doi.org/10.5194/gmd-11-195-2018, https://doi.org/10.5194/gmd-11-195-2018, 2018
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Previous studies indicate that fluxes of heat, water, and carbon between the land surface and atmosphere are substantially more predictable than the performance of the current crop of land surface models would indicate. This study uses simple empirical models to estimate the amount of useful information in meteorological forcings that is available for predicting land surface fluxes. These models can be used as benchmarks for land surface models and may help identify areas ripe for improvement.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
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This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Martin G. De Kauwe, Belinda E. Medlyn, Jürgen Knauer, and Christopher A. Williams
Biogeosciences, 14, 4435–4453, https://doi.org/10.5194/bg-14-4435-2017, https://doi.org/10.5194/bg-14-4435-2017, 2017
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Understanding the sensitivity of transpiration to stomatal conductance is critical to simulating the water cycle. This sensitivity is a function of the degree of coupling between the vegetation and the atmosphere. We combined an extensive literature summary with estimates of coupling derived from FLUXNET data. We found notable departures from the values previously reported. These data form a model benchmarking metric to test existing coupling assumptions.
Richard Wartenburger, Martin Hirschi, Markus G. Donat, Peter Greve, Andy J. Pitman, and Sonia I. Seneviratne
Geosci. Model Dev., 10, 3609–3634, https://doi.org/10.5194/gmd-10-3609-2017, https://doi.org/10.5194/gmd-10-3609-2017, 2017
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This article analyses regional changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. Readers are encouraged to use the online tool for visualization of specific indices of interest, e.g. to assess their response to 1.5 or 2 °C global warming.
Seyed Hamed Alemohammad, Bin Fang, Alexandra G. Konings, Filipe Aires, Julia K. Green, Jana Kolassa, Diego Miralles, Catherine Prigent, and Pierre Gentine
Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, https://doi.org/10.5194/bg-14-4101-2017, 2017
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Water, Energy, and Carbon with Artificial Neural Networks (WECANN) is a statistically based estimate of global surface latent and sensible heat fluxes and gross primary productivity. The retrieval uses six remotely sensed observations as input, including the solar-induced fluorescence. WECANN provides estimates on a 1° × 1° geographic grid and on a monthly time scale and outperforms other global products in capturing the seasonality of the fluxes when compared to eddy covariance tower data.
Anna M. Ukkola, Ned Haughton, Martin G. De Kauwe, Gab Abramowitz, and Andy J. Pitman
Geosci. Model Dev., 10, 3379–3390, https://doi.org/10.5194/gmd-10-3379-2017, https://doi.org/10.5194/gmd-10-3379-2017, 2017
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Flux towers measure energy, carbon dioxide and water vapour fluxes. These data have become essential for evaluating land surface models (LSMs) – key tools for projecting future climate change. However, these data as released are not immediately usable with LSMs and must be post-processed to change units, screened for missing data and gap-filling. We present an open-source R package that transforms flux tower measurements into a format directly usable by LSMs.
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017, https://doi.org/10.5194/hess-21-3879-2017, 2017
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We examine the opportunities and challenges that technological advances in Earth observation will present to the hydrological community. From advanced space-based sensors to unmanned aerial vehicles and ground-based distributed networks, these emergent systems are set to revolutionize our understanding and interpretation of hydrological and related processes.
Rachel M. Law, Tilo Ziehn, Richard J. Matear, Andrew Lenton, Matthew A. Chamberlain, Lauren E. Stevens, Ying-Ping Wang, Jhan Srbinovsky, Daohua Bi, Hailin Yan, and Peter F. Vohralik
Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, https://doi.org/10.5194/gmd-10-2567-2017, 2017
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The paper describes a version of the Australian Community Climate and Earth System Simulator that has been enabled to simulate the carbon cycle, which is designated ACCESS-ESM1. The model performance for pre-industrial conditions is assessed and land and ocean carbon fluxes are found to be simulated realistically.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
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Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Caitlin E. Moore, Tim Brown, Trevor F. Keenan, Remko A. Duursma, Albert I. J. M. van Dijk, Jason Beringer, Darius Culvenor, Bradley Evans, Alfredo Huete, Lindsay B. Hutley, Stefan Maier, Natalia Restrepo-Coupe, Oliver Sonnentag, Alison Specht, Jeffrey R. Taylor, Eva van Gorsel, and Michael J. Liddell
Biogeosciences, 13, 5085–5102, https://doi.org/10.5194/bg-13-5085-2016, https://doi.org/10.5194/bg-13-5085-2016, 2016
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Australian vegetation phenology is highly variable due to the diversity of ecosystems on the continent. We explore continental-scale variability using satellite remote sensing by broadly classifying areas as seasonal, non-seasonal, or irregularly seasonal. We also examine ecosystem-scale phenology using phenocams and show that some broadly non-seasonal ecosystems do display phenological variability. Overall, phenocams are useful for understanding ecosystem-scale Australian vegetation phenology.
Eva A. Kowalczyk, Lauren E. Stevens, Rachel M. Law, Ian N. Harman, Martin Dix, Charmaine N. Franklin, and Ying-Ping Wang
Geosci. Model Dev., 9, 2771–2791, https://doi.org/10.5194/gmd-9-2771-2016, https://doi.org/10.5194/gmd-9-2771-2016, 2016
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This paper compares two ACCESS model versions that differ only in their land surface scheme. Differences in the simulated present-day climate are attributed to differences in the representation of various land surface processes.
Rashid Rafique, Jianyang Xia, Oleksandra Hararuk, Ghassem R. Asrar, Guoyong Leng, Yingping Wang, and Yiqi Luo
Earth Syst. Dynam., 7, 649–658, https://doi.org/10.5194/esd-7-649-2016, https://doi.org/10.5194/esd-7-649-2016, 2016
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Traceability analysis was used to diagnose the causes of differences in simulating ecosystem carbon storage capacity between two land models: CLMA-CASA and CABLE. Results showed that the simulated ecosystem carbon storage capacity is largely influenced by the photosynthesis parameterization, residence time and organic matter decomposition.
Anna M. Ukkola, Andy J. Pitman, Mark Decker, Martin G. De Kauwe, Gab Abramowitz, Jatin Kala, and Ying-Ping Wang
Hydrol. Earth Syst. Sci., 20, 2403–2419, https://doi.org/10.5194/hess-20-2403-2016, https://doi.org/10.5194/hess-20-2403-2016, 2016
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
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In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
D. Michel, C. Jiménez, D. G. Miralles, M. Jung, M. Hirschi, A. Ershadi, B. Martens, M. F. McCabe, J. B. Fisher, Q. Mu, S. I. Seneviratne, E. F. Wood, and D. Fernández-Prieto
Hydrol. Earth Syst. Sci., 20, 803–822, https://doi.org/10.5194/hess-20-803-2016, https://doi.org/10.5194/hess-20-803-2016, 2016
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In this study a common reference input data set from satellite and in situ data is used to run four established evapotranspiration (ET) algorithms using sub-daily and daily input on a tower scale as a testbed for a global ET product. The PT-JPL model and GLEAM provide the best performance for satellite and in situ forcing as well as for the different temporal resolutions. PM-MOD and SEBS perform less well: the PM-MOD model generally underestimates, while SEBS generally overestimates ET.
Y. P. Wang, J. Jiang, B. Chen-Charpentier, F. B. Agusto, A. Hastings, F. Hoffman, M. Rasmussen, M. J. Smith, K. Todd-Brown, Y. Wang, X. Xu, and Y. Q. Luo
Biogeosciences, 13, 887–902, https://doi.org/10.5194/bg-13-887-2016, https://doi.org/10.5194/bg-13-887-2016, 2016
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Comparing two nonlinear microbial models, we found that,
in response to warming, soil C decreases in one model but can increase or decrease in the other model, and sensitivity of priming response to carbon input increases with soil T in one model but decreases in the other model
Significance: these differences in the responses can be used to discern which model is more realistic, which will improve our understanding of the significance of soil microbial processes in the terrestrial C cycle.
M. F. McCabe, A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, and E. F. Wood
Geosci. Model Dev., 9, 283–305, https://doi.org/10.5194/gmd-9-283-2016, https://doi.org/10.5194/gmd-9-283-2016, 2016
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In an effort to develop a global terrestrial evaporation product, four models were forced using both a tower and grid-based data set. Comparisons against flux-tower observations from different biome and land cover types show considerable inter-model variability and sensitivity to forcing type. Results suggest that no single model is able to capture expected flux patterns and response. It is suggested that a multi-model ensemble is likely to provide a more stable long-term flux estimate.
M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, https://doi.org/10.5194/bg-12-7503-2015, 2015
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Future climate change has the potential to increase drought in many regions of the globe. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art models currently assume the same drought sensitivity for all vegetation. Our results indicate that models will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
J. Kala, M. G. De Kauwe, A. J. Pitman, R. Lorenz, B. E. Medlyn, Y.-P Wang, Y.-S Lin, and G. Abramowitz
Geosci. Model Dev., 8, 3877–3889, https://doi.org/10.5194/gmd-8-3877-2015, https://doi.org/10.5194/gmd-8-3877-2015, 2015
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We implement a new stomatal conductance scheme within a land surface model coupled to a global climate model. The new model differs from the default in that it allows model parameters to vary by the different plant functional types, derived from global synthesis of observations. We show that the new scheme results in improvements in the model climatology and improves existing biases in warm temperature extremes by up to 10-20% over the boreal forests during summer.
M. Decker, A. Pitman, and J. Evans
Hydrol. Earth Syst. Sci., 19, 3433–3447, https://doi.org/10.5194/hess-19-3433-2015, https://doi.org/10.5194/hess-19-3433-2015, 2015
A. I. Stegehuis, R. Vautard, P. Ciais, A. J. Teuling, D. G. Miralles, and M. Wild
Geosci. Model Dev., 8, 2285–2298, https://doi.org/10.5194/gmd-8-2285-2015, https://doi.org/10.5194/gmd-8-2285-2015, 2015
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Many climate models have difficulties in properly reproducing climate extremes such as heat wave conditions. We use a regional climate model with different atmospheric physics schemes to simulate the heat wave events of 2003 in western Europe and 2010 in Russia. The five best-performing and diverse physics scheme combinations may be used in the future to perform heat wave analysis and to investigate the impact of climate change in summer in Europe.
I. C. Prentice, X. Liang, B. E. Medlyn, and Y.-P. Wang
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, https://doi.org/10.5194/acp-15-5987-2015, 2015
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Land surface models (LSMs) describe how carbon and water fluxes react to environmental change. They are key component of climate models, yet they differ enormously. Many perform poorly, despite having many parameters. We outline a development strategy emphasizing robustness, reliability and realism, none of which is guaranteed by complexity alone. We propose multiple constraints, benchmarking and data assimilation, and representing unresolved processes stochastically, as tools in this endeavour.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
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Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Biogeosciences, 11, 6999–7008, https://doi.org/10.5194/bg-11-6999-2014, https://doi.org/10.5194/bg-11-6999-2014, 2014
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We use a reduced complexity soil organic carbon (SOC) model to address the influence of two parameters on the response of SOC stocks to climate change: baseline turnover time (k) and temperature sensitivity of decomposition (Q10). In our model, k determines SOC stocks and the magnitude of the response to climate change (from 1850 to 2100 under RCP 8.5) while Q10 drives its sign. We dismiss unlikely simulations using global SOC data to reduce the uncertainty in projections and parameter values.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Geosci. Model Dev., 7, 2683–2692, https://doi.org/10.5194/gmd-7-2683-2014, https://doi.org/10.5194/gmd-7-2683-2014, 2014
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Pre-industrial soil organic carbon (SOC) stocks vary 6-fold in models used in the 5th IPCC Assessment Report. This paper shows that this range is largely determined by model-specific responses of microbal decomposition during the equilibration procedure. As SOC stocks are maintained through the present and to 2100 almost unchanged, we propose that current SOC observations could be used to constrain this equilibration procedure and thereby reduce the uncertainty in climate change projections.
J. Kala, J. P. Evans, A. J. Pitman, C. B. Schaaf, M. Decker, C. Carouge, D. Mocko, and Q. Sun
Geosci. Model Dev., 7, 2121–2140, https://doi.org/10.5194/gmd-7-2121-2014, https://doi.org/10.5194/gmd-7-2121-2014, 2014
B. P. Guillod, B. Orlowsky, D. Miralles, A. J. Teuling, P. D. Blanken, N. Buchmann, P. Ciais, M. Ek, K. L. Findell, P. Gentine, B. R. Lintner, R. L. Scott, B. Van den Hurk, and S. I. Seneviratne
Atmos. Chem. Phys., 14, 8343–8367, https://doi.org/10.5194/acp-14-8343-2014, https://doi.org/10.5194/acp-14-8343-2014, 2014
Y. P. Wang, B. C. Chen, W. R. Wieder, M. Leite, B. E. Medlyn, M. Rasmussen, M. J. Smith, F. B. Agusto, F. Hoffman, and Y. Q. Luo
Biogeosciences, 11, 1817–1831, https://doi.org/10.5194/bg-11-1817-2014, https://doi.org/10.5194/bg-11-1817-2014, 2014
R. Lorenz, A. J. Pitman, M. G. Donat, A. L. Hirsch, J. Kala, E. A. Kowalczyk, R. M. Law, and J. Srbinovsky
Geosci. Model Dev., 7, 545–567, https://doi.org/10.5194/gmd-7-545-2014, https://doi.org/10.5194/gmd-7-545-2014, 2014
J.-F. Exbrayat, A. J. Pitman, Q. Zhang, G. Abramowitz, and Y.-P. Wang
Biogeosciences, 10, 7095–7108, https://doi.org/10.5194/bg-10-7095-2013, https://doi.org/10.5194/bg-10-7095-2013, 2013
B. Mueller, M. Hirschi, C. Jimenez, P. Ciais, P. A. Dirmeyer, A. J. Dolman, J. B. Fisher, M. Jung, F. Ludwig, F. Maignan, D. G. Miralles, M. F. McCabe, M. Reichstein, J. Sheffield, K. Wang, E. F. Wood, Y. Zhang, and S. I. Seneviratne
Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, https://doi.org/10.5194/hess-17-3707-2013, 2013
Q. Zhang, A. J. Pitman, Y. P. Wang, Y. J. Dai, and P. J. Lawrence
Earth Syst. Dynam., 4, 333–345, https://doi.org/10.5194/esd-4-333-2013, https://doi.org/10.5194/esd-4-333-2013, 2013
Related subject area
Climate and Earth system modeling
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
The Regional Climate-Chemistry-Ecology Coupling Model RegCM-Chem (v4.6)-YIBs (v1.0): Development and Application
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
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.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Carl Svenhag, Moa Kristina Sporre, Tinja Olenius, Daniel Yazgi, Sara Marie Blichner, Lars Peter Nieradzik, and Pontus Roldin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2665, https://doi.org/10.5194/egusphere-2023-2665, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we showed that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacted the number of particles in the air and clouds. It also changed the radiation balance in the same magnitude as man-made greenhouse emissions.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
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.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
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Short summary
Stomatal conductance affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the CABLE land surface model (LSM). The new implementation resulted in a large reduction in the annual fluxes of transpiration across evergreen needleleaf, tundra and C4 grass regions. We conclude that optimisation theory can yield a tractable approach to predicting stomatal conductance in LSMs.
Stomatal conductance affects the fluxes of carbon, energy and water between the vegetated land...
