Articles | Volume 8, issue 2
https://doi.org/10.5194/gmd-8-261-2015
© 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-8-261-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model experiment description paper
| 
11 Feb 2015
Model experiment description paper |
| 11 Feb 2015
The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)
J. Elliott
CORRESPONDING AUTHOR
University of Chicago & Argonne Natl. Lab Computation Institute, Chicago, Illinois, USA
C. Müller
Potsdam Institute for Climate Impact Research, Potsdam, Germany
D. Deryng
Tyndall Centre, University of East Anglia, Norwich, UK
J. Chryssanthacopoulos
Columbia University Center for Climate Systems Research, New York, New York, USA
K. J. Boote
University of Florida Department of Agronomy, Gainesville, Florida, USA
M. Büchner
Potsdam Institute for Climate Impact Research, Potsdam, Germany
I. Foster
University of Chicago & Argonne Natl. Lab Computation Institute, Chicago, Illinois, USA
M. Glotter
University of Chicago Department of Geophysical Science, Chicago, Illinois, USA
J. Heinke
Potsdam Institute for Climate Impact Research, Potsdam, Germany
International Livestock Research Institute, Nairobi, Kenya
CSIRO (Commonwealth Scientific and Industrial Research Organization), St Lucia QLD 4067, Australia
T. Iizumi
National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan
R. C. Izaurralde
University of Maryland Dept. of Geographical Sciences, College Park, Maryland, USA
N. D. Mueller
Harvard University Center for the Environment, Cambridge, Massachusetts, USA
D. K. Ray
University of Minnesota Institute for the Environment, Saint Paul, Minnesota, USA
C. Rosenzweig
NASA Goddard Institute for Space Studies, New York, New York, USA
A. C. Ruane
NASA Goddard Institute for Space Studies, New York, New York, USA
J. Sheffield
Princeton University Dept. Civil & Environ. Engineering, Princeton, New Jersey, USA
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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.
Tokuta Yokohata, Tsuguki Kinoshita, Gen Sakurai, Yadu Pokhrel, Akihiko Ito, Masashi Okada, Yusuke Satoh, Etsushi Kato, Tomoko Nitta, Shinichiro Fujimori, Farshid Felfelani, Yoshimitsu Masaki, Toshichika Iizumi, Motoki Nishimori, Naota Hanasaki, Kiyoshi Takahashi, Yoshiki Yamagata, and Seita Emori
Geosci. Model Dev., 13, 4713–4747, https://doi.org/10.5194/gmd-13-4713-2020, https://doi.org/10.5194/gmd-13-4713-2020, 2020
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The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balances, human water management, and crop growth incorporates a land-use decision-making model based on economic activities. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
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Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923, https://doi.org/10.5194/gmd-13-3905-2020, https://doi.org/10.5194/gmd-13-3905-2020, 2020
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Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Thomas A. M. Pugh, Tim Rademacher, Sarah L. Shafer, Jörg Steinkamp, Jonathan Barichivich, Brian Beckage, Vanessa Haverd, Anna Harper, Jens Heinke, Kazuya Nishina, Anja Rammig, Hisashi Sato, Almut Arneth, Stijn Hantson, Thomas Hickler, Markus Kautz, Benjamin Quesada, Benjamin Smith, and Kirsten Thonicke
Biogeosciences, 17, 3961–3989, https://doi.org/10.5194/bg-17-3961-2020, https://doi.org/10.5194/bg-17-3961-2020, 2020
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The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years for the global mean for 1985–2014. Future projections do not give consistent results, but 13 model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce large current uncertainty.
Christopher P. O. Reyer, Ramiro Silveyra Gonzalez, Klara Dolos, Florian Hartig, Ylva Hauf, Matthias Noack, Petra Lasch-Born, Thomas Rötzer, Hans Pretzsch, Henning Meesenburg, Stefan Fleck, Markus Wagner, Andreas Bolte, Tanja G. M. Sanders, Pasi Kolari, Annikki Mäkelä, Timo Vesala, Ivan Mammarella, Jukka Pumpanen, Alessio Collalti, Carlo Trotta, Giorgio Matteucci, Ettore D'Andrea, Lenka Foltýnová, Jan Krejza, Andreas Ibrom, Kim Pilegaard, Denis Loustau, Jean-Marc Bonnefond, Paul Berbigier, Delphine Picart, Sébastien Lafont, Michael Dietze, David Cameron, Massimo Vieno, Hanqin Tian, Alicia Palacios-Orueta, Victor Cicuendez, Laura Recuero, Klaus Wiese, Matthias Büchner, Stefan Lange, Jan Volkholz, Hyungjun Kim, Joanna A. Horemans, Friedrich Bohn, Jörg Steinkamp, Alexander Chikalanov, Graham P. Weedon, Justin Sheffield, Flurin Babst, Iliusi Vega del Valle, Felicitas Suckow, Simon Martel, Mats Mahnken, Martin Gutsch, and Katja Frieler
Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, https://doi.org/10.5194/essd-12-1295-2020, 2020
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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale to support systematic model intercomparisons and model development in Europe.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Juraj Balkovic, Philippe Ciais, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, Munir Hoffmann, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Nikolay Khabarov, Marian Koch, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Xuhui Wang, Karina Williams, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 2315–2336, https://doi.org/10.5194/gmd-13-2315-2020, https://doi.org/10.5194/gmd-13-2315-2020, 2020
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Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Crop models, which represent plant biology, are necessary tools for this purpose since they allow representing future climate, farmer choices, and new agricultural geographies. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, under the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to evaluate and improve crop models.
Matias Heino, Joseph H. A. Guillaume, Christoph Müller, Toshichika Iizumi, and Matti Kummu
Earth Syst. Dynam., 11, 113–128, https://doi.org/10.5194/esd-11-113-2020, https://doi.org/10.5194/esd-11-113-2020, 2020
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In this study, we analyse the impacts of three major climate oscillations on global crop production. Our results show that maize, rice, soybean, and wheat yields are influenced by climate oscillations to a wide extent and in several important crop-producing regions. We observe larger impacts if crops are rainfed or fully fertilized, while irrigation tends to mitigate the impacts. These results can potentially help to increase the resilience of the global food system to climate-related shocks.
Maarten C. Braakhekke, Jonathan C. Doelman, Peter Baas, Christoph Müller, Sibyll Schaphoff, Elke Stehfest, and Detlef P. van Vuuren
Earth Syst. Dynam., 10, 617–630, https://doi.org/10.5194/esd-10-617-2019, https://doi.org/10.5194/esd-10-617-2019, 2019
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We developed a computer model that simulates forests plantations at global scale and how fast such forests can take up CO2 from the atmosphere. Using this new model, we performed simulations for a scenario in which a large fraction (14 %) of global croplands and pastures are either converted to planted forests or natural forests. We find that planted forests take up CO2 substantially faster than natural forests and are therefore a viable strategy for reducing climate change.
Bruno Ringeval, Marko Kvakić, Laurent Augusto, Philippe Ciais, Daniel Goll, Nathaniel D. Mueller, Christoph Müller, Thomas Nesme, Nicolas Vuichard, Xuhui Wang, and Sylvain Pellerin
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-298, https://doi.org/10.5194/bg-2019-298, 2019
Preprint withdrawn
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Crossed fertilization additions lead to the definition of nutrient interaction categories. However, the implications of such categories in terms of nutrient interaction modeling are not clear. We developed a theoretical analysis of nitrogen and phosphorus fertilization experiments, then applied it to current estimates of nutrient limitation in cropland. We found that a true co-limitation could affect up to 42 % of the global maize area when using a given formalism of nutrient interaction.
Femke Lutz, Tobias Herzfeld, Jens Heinke, Susanne Rolinski, Sibyll Schaphoff, Werner von Bloh, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, https://doi.org/10.5194/gmd-12-2419-2019, 2019
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Tillage practices are under-represented in global biogeochemical models so that assessments of agricultural greenhouse gas emissions and climate mitigation options are hampered. We describe the implementation of tillage modules into the model LPJmL5.0, including multiple feedbacks between soil water, nitrogen, and productivity. By comparing simulation results with observational data, we show that the model can reproduce reported tillage effects on carbon and water dynamics and crop yields.
Vera Porwollik, Susanne Rolinski, Jens Heinke, and Christoph Müller
Earth Syst. Sci. Data, 11, 823–843, https://doi.org/10.5194/essd-11-823-2019, https://doi.org/10.5194/essd-11-823-2019, 2019
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This study describes the generation of a classification and the global spatially explicit mapping of six crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. The identified tillage systems including a downscale algorithm of national Conservation Agriculture area values were allocated to crop-specific cropland areas with a resolution of 5 arcmin.
Abigail Snyder, Katherine V. Calvin, Meridel Phillips, and Alex C. Ruane
Geosci. Model Dev., 12, 1319–1350, https://doi.org/10.5194/gmd-12-1319-2019, https://doi.org/10.5194/gmd-12-1319-2019, 2019
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Future changes in Earth system state will impact agricultural yields and therefore the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields, but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as GCAM and other similar-in-scale models. This work describes a new crop yield change emulator, Persephone, that can capture yield changes in a computationally efficient way.
Jens Heinke, Christoph Müller, Mats Lannerstad, Dieter Gerten, and Wolfgang Lucht
Earth Syst. Dynam., 10, 205–217, https://doi.org/10.5194/esd-10-205-2019, https://doi.org/10.5194/esd-10-205-2019, 2019
Anja Rammig, Jens Heinke, Florian Hofhansl, Hans Verbeeck, Timothy R. Baker, Bradley Christoffersen, Philippe Ciais, Hannes De Deurwaerder, Katrin Fleischer, David Galbraith, Matthieu Guimberteau, Andreas Huth, Michelle Johnson, Bart Krujit, Fanny Langerwisch, Patrick Meir, Phillip Papastefanou, Gilvan Sampaio, Kirsten Thonicke, Celso von Randow, Christian Zang, and Edna Rödig
Geosci. Model Dev., 11, 5203–5215, https://doi.org/10.5194/gmd-11-5203-2018, https://doi.org/10.5194/gmd-11-5203-2018, 2018
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We propose a generic approach for a pixel-to-point comparison applicable for evaluation of models and remote-sensing products. We provide statistical measures accounting for the uncertainty in ecosystem variables. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest.
Werner von Bloh, Sibyll Schaphoff, Christoph Müller, Susanne Rolinski, Katharina Waha, and Sönke Zaehle
Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, https://doi.org/10.5194/gmd-11-2789-2018, 2018
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The dynamics of the terrestrial carbon cycle are of central importance for Earth system science. Nutrient limitations, especially from nitrogen, are important constraints on vegetation growth and the terrestrial carbon cycle. We extended the well-established global vegetation, hydrology, and crop model LPJmL with a nitrogen cycle. We find significant improvement in global patterns of crop productivity. Regional differences in crop productivity can now be largely reproduced by the model.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
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Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Sibyll Schaphoff, Werner von Bloh, Anja Rammig, Kirsten Thonicke, Hester Biemans, Matthias Forkel, Dieter Gerten, Jens Heinke, Jonas Jägermeyr, Jürgen Knauer, Fanny Langerwisch, Wolfgang Lucht, Christoph Müller, Susanne Rolinski, and Katharina Waha
Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, https://doi.org/10.5194/gmd-11-1343-2018, 2018
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Here we provide a comprehensive model description of a global terrestrial biosphere model, named LPJmL4, incorporating the carbon and water cycle and the quantification of agricultural production. The model allows for the consistent and joint quantification of climate and land use change impacts on the biosphere. The model represents the key ecosystem functions, but also the influence of humans on the biosphere. It comes with an evaluation paper to demonstrate the credibility of LPJmL4.
Sibyll Schaphoff, Matthias Forkel, Christoph Müller, Jürgen Knauer, Werner von Bloh, Dieter Gerten, Jonas Jägermeyr, Wolfgang Lucht, Anja Rammig, Kirsten Thonicke, and Katharina Waha
Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, https://doi.org/10.5194/gmd-11-1377-2018, 2018
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Here we provide a comprehensive evaluation of the now launched version 4.0 of the LPJmL biosphere, water, and agricultural model. The article is the second part to a comprehensive description of the LPJmL4 model. We have evaluated the model against various datasets of satellite observations, agricultural statistics, and in situ measurements by applying a range of metrics. We are able to show that the LPJmL4 model simulates many parameters and relations reasonably.
Andreas Marx, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Niko Wanders, Matthias Zink, Eric F. Wood, Ming Pan, Justin Sheffield, and Luis Samaniego
Hydrol. Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, https://doi.org/10.5194/hess-22-1017-2018, 2018
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Hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K). The multi-model ensemble results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean, while they increase in the Alpine and Northern regions. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Adaptation should make use of change and uncertainty information.
Susanne Rolinski, Christoph Müller, Jens Heinke, Isabelle Weindl, Anne Biewald, Benjamin Leon Bodirsky, Alberte Bondeau, Eltje R. Boons-Prins, Alexander F. Bouwman, Peter A. Leffelaar, Johnny A. te Roller, Sibyll Schaphoff, and Kirsten Thonicke
Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, https://doi.org/10.5194/gmd-11-429-2018, 2018
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One-third of the global land area is covered with grasslands which are grazed by or mowed for livestock feed. These areas contribute significantly to the carbon capture from the atmosphere when managed sensibly. To assess the effect of this management, we included different options of grazing and mowing into the global model LPJmL 3.6. We found in polar regions even low grazing pressure leads to soil carbon loss whereas in temperate regions up to 1.4 livestock units per hectare can be sustained.
John Musau, Sopan Patil, Justin Sheffield, and Michael Marshall
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2017-123, https://doi.org/10.5194/esd-2017-123, 2018
Manuscript not accepted for further review
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Three decades LAI data indicates diverse and often non-stationary vegetation changes in East Africa. Significant increase in vegetation variance is indicated in most of the region which is positively correlated to the variance of climate anomalies. The vegetation resistance to short-term drought and its memory effect are mainly positive and significant with noteworthy variations across landcover types. Further analysis is required to separated human-induced and climate-caused vegetation changes.
Yu Zhang, Ming Pan, Justin Sheffield, Amanda L. Siemann, Colby K. Fisher, Miaoling Liang, Hylke E. Beck, Niko Wanders, Rosalyn F. MacCracken, Paul R. Houser, Tian Zhou, Dennis P. Lettenmaier, Rachel T. Pinker, Janice Bytheway, Christian D. Kummerow, and Eric F. Wood
Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, https://doi.org/10.5194/hess-22-241-2018, 2018
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A global data record for all four terrestrial water budget variables (precipitation, evapotranspiration, runoff, and total water storage change) at 0.5° resolution and monthly scale for the period of 1984–2010 is developed by optimally merging a series of remote sensing products, in situ measurements, land surface model outputs, and atmospheric reanalysis estimates and enforcing the mass balance of water. Initial validations show the data record is reliable for climate related analysis.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil, Kerry Emanuel, Tobias Geiger, Kate Halladay, George Hurtt, Matthias Mengel, Daisuke Murakami, Sebastian Ostberg, Alexander Popp, Riccardo Riva, Miodrag Stevanovic, Tatsuo Suzuki, Jan Volkholz, Eleanor Burke, Philippe Ciais, Kristie Ebi, Tyler D. Eddy, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Christian Hof, Veronika Huber, Jonas Jägermeyr, Valentina Krysanova, Rafael Marcé, Hannes Müller Schmied, Ioanna Mouratiadou, Don Pierson, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Matthias F. Biber, Richard A. Betts, Benjamin Leon Bodirsky, Delphine Deryng, Steve Frolking, Chris D. Jones, Heike K. Lotze, Hermann Lotze-Campen, Ritvik Sahajpal, Kirsten Thonicke, Hanqin Tian, and Yoshiki Yamagata
Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
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This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agiculture, energy, and coastal infrastructure.
Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, Christian Folberth, Michael Glotter, Steven Hoek, Toshichika Iizumi, Roberto C. Izaurralde, Curtis Jones, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Stefan Olin, Thomas A. M. Pugh, Deepak K. Ray, Ashwan Reddy, Cynthia Rosenzweig, Alex C. Ruane, Gen Sakurai, Erwin Schmid, Rastislav Skalsky, Carol X. Song, Xuhui Wang, Allard de Wit, and Hong Yang
Geosci. Model Dev., 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017, https://doi.org/10.5194/gmd-10-1403-2017, 2017
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Crop models are increasingly used in climate change impact research and integrated assessments. For the Agricultural Model Intercomparison and Improvement Project (AgMIP), 14 global gridded crop models (GGCMs) have supplied crop yield simulations (1980–2010) for maize, wheat, rice and soybean. We evaluate the performance of these models against observational data at global, national and grid cell level. We propose an open-access benchmark system against which future model versions can be tested.
Michael Marshall, Michael Norton-Griffiths, Harvey Herr, Richard Lamprey, Justin Sheffield, Tor Vagen, and Joseph Okotto-Okotto
Earth Syst. Dynam., 8, 55–73, https://doi.org/10.5194/esd-8-55-2017, https://doi.org/10.5194/esd-8-55-2017, 2017
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The transition of land from one cover type to another can adversely affect the Earth system. A growing body of research aims to map these transitions in space and time to better understand the impacts. Here we develop a statistical model that is parameterized by socio-ecological geospatial data and extensive aerial/ground surveys to visualize and interpret these transitions on an annual basis for 30 years in Kenya. Future work will use this method to project land suitability across Africa.
Christian Folberth, Joshua Elliott, Christoph Müller, Juraj Balkovic, James Chryssanthacopoulos, Roberto C. Izaurralde, Curtis D. Jones, Nikolay Khabarov, Wenfeng Liu, Ashwan Reddy, Erwin Schmid, Rastislav Skalský, Hong Yang, Almut Arneth, Philippe Ciais, Delphine Deryng, Peter J. Lawrence, Stefan Olin, Thomas A. M. Pugh, Alex C. Ruane, and Xuhui Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-527, https://doi.org/10.5194/bg-2016-527, 2016
Manuscript not accepted for further review
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Global crop models differ in numerous aspects such as algorithms, parameterization, input data, and management assumptions. This study compares five global crop model frameworks, all based on the same field-scale model, to identify differences induced by the latter three. Results indicate that foremost nutrient supply, soil handling, and crop management induce substantial differences in crop yield estimates whereas crop cultivars primarily result in scaling of yield levels.
John Musau, Sopan Patil, Justin Sheffield, and Michael Marshall
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-502, https://doi.org/10.5194/hess-2016-502, 2016
Manuscript not accepted for further review
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An assessment of vegetation-climate relations over East Africa is presented. LAI trends in Southern Ethiopia through Central Kenya into Central Tanzania show persistent decrease. Precipitation exerts widespread positive forcing on vegetation. North Uganda shows high LAI increase. Positive vegetation feedback on precipitation is dominant while a stronger negative forcing on Tmin is shown. Vegetation-climate interactions show strong spatial dependence. Land cover types influence the interractions.
Alex C. Ruane, Claas Teichmann, Nigel W. Arnell, Timothy R. Carter, Kristie L. Ebi, Katja Frieler, Clare M. Goodess, Bruce Hewitson, Radley Horton, R. Sari Kovats, Heike K. Lotze, Linda O. Mearns, Antonio Navarra, Dennis S. Ojima, Keywan Riahi, Cynthia Rosenzweig, Matthias Themessl, and Katharine Vincent
Geosci. Model Dev., 9, 3493–3515, https://doi.org/10.5194/gmd-9-3493-2016, https://doi.org/10.5194/gmd-9-3493-2016, 2016
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The Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) Advisory Board for CMIP6 was created to improve communications between communities that apply climate model output for societal benefit and the climate model centers. This manuscript describes the establishment of the VIACS Advisory Board as a coherent avenue for communication utilizing leading networks, experts, and programs; results of initial interactions during the development of CMIP6; and its potential next activities.
Anne F. Van Loon, Kerstin Stahl, Giuliano Di Baldassarre, Julian Clark, Sally Rangecroft, Niko Wanders, Tom Gleeson, Albert I. J. M. Van Dijk, Lena M. Tallaksen, Jamie Hannaford, Remko Uijlenhoet, Adriaan J. Teuling, David M. Hannah, Justin Sheffield, Mark Svoboda, Boud Verbeiren, Thorsten Wagener, and Henny A. J. Van Lanen
Hydrol. Earth Syst. Sci., 20, 3631–3650, https://doi.org/10.5194/hess-20-3631-2016, https://doi.org/10.5194/hess-20-3631-2016, 2016
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In the Anthropocene, drought cannot be viewed as a natural hazard independent of people. Drought can be alleviated or made worse by human activities and drought impacts are dependent on a myriad of factors. In this paper, we identify research gaps and suggest a framework that will allow us to adequately analyse and manage drought in the Anthropocene. We need to focus on attribution of drought to different drivers, linking drought to its impacts, and feedbacks between drought and society.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
Wolfgang Buermann, Claudie Beaulieu, Bikash Parida, David Medvigy, George J. Collatz, Justin Sheffield, and Jorge L. Sarmiento
Biogeosciences, 13, 1597–1607, https://doi.org/10.5194/bg-13-1597-2016, https://doi.org/10.5194/bg-13-1597-2016, 2016
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Recent analyses of the global carbon budget found a substantial increase in the land sink in the late 1980s whose origin remains unknown. Consistent with this shift, we find that plant growth increased in the late 1980s especially in Eurasia and northern Africa. There, climatic constraints on plant growth have eased possibly due to linked climate modes in the North Atlantic. Better understanding of North Atlantic climate may be essential for more credible projections of the land carbon sink.
S. Sadri, J. Kam, and J. Sheffield
Hydrol. Earth Syst. Sci., 20, 633–649, https://doi.org/10.5194/hess-20-633-2016, https://doi.org/10.5194/hess-20-633-2016, 2016
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Low flows are a critical part of the river flow regime but little is known about how they are changing in response to human influences and climate. We analyzed low flow records across the eastern US and identified sites that were minimally influenced by human activities. We found a general increasing trend in low flows across the northeast and decreasing trend across the southeast that are likely driven by changes in climate. The results have implications for how we manage our water resources.
K. Frieler, A. Levermann, J. Elliott, J. Heinke, A. Arneth, M. F. P. Bierkens, P. Ciais, D. B. Clark, D. Deryng, P. Döll, P. Falloon, B. Fekete, C. Folberth, A. D. Friend, C. Gellhorn, S. N. Gosling, I. Haddeland, N. Khabarov, M. Lomas, Y. Masaki, K. Nishina, K. Neumann, T. Oki, R. Pavlick, A. C. Ruane, E. Schmid, C. Schmitz, T. Stacke, E. Stehfest, Q. Tang, D. Wisser, V. Huber, F. Piontek, L. Warszawski, J. Schewe, H. Lotze-Campen, and H. J. Schellnhuber
Earth Syst. Dynam., 6, 447–460, https://doi.org/10.5194/esd-6-447-2015, https://doi.org/10.5194/esd-6-447-2015, 2015
J. Jägermeyr, D. Gerten, J. Heinke, S. Schaphoff, M. Kummu, and W. Lucht
Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, https://doi.org/10.5194/hess-19-3073-2015, 2015
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We present a process-based simulation of global irrigation systems for the world’s major crop types. This study advances the global quantification of irrigation systems while providing a framework for assessing potential future transitions in these systems, a prerequisite for refined simulation of crop yields under climate change. We reveal for many river basins the potential for sizeable water savings and related increases in water productivity through irrigation improvements.
D. C. Zemp, C.-F. Schleussner, H. M. J. Barbosa, R. J. van der Ent, J. F. Donges, J. Heinke, G. Sampaio, and A. Rammig
Atmos. Chem. Phys., 14, 13337–13359, https://doi.org/10.5194/acp-14-13337-2014, https://doi.org/10.5194/acp-14-13337-2014, 2014
V. Huber, H. J. Schellnhuber, N. W. Arnell, K. Frieler, A. D. Friend, D. Gerten, I. Haddeland, P. Kabat, H. Lotze-Campen, W. Lucht, M. Parry, F. Piontek, C. Rosenzweig, J. Schewe, and L. Warszawski
Earth Syst. Dynam., 5, 399–408, https://doi.org/10.5194/esd-5-399-2014, https://doi.org/10.5194/esd-5-399-2014, 2014
L. Batlle-Bayer, B. J. J. M. van den Hurk, C. Müller, and J. van Minnen
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esdd-5-585-2014, https://doi.org/10.5194/esdd-5-585-2014, 2014
Revised manuscript has not been submitted
S. Manfreda, L. Brocca, T. Moramarco, F. Melone, and J. Sheffield
Hydrol. Earth Syst. Sci., 18, 1199–1212, https://doi.org/10.5194/hess-18-1199-2014, https://doi.org/10.5194/hess-18-1199-2014, 2014
M. Kummu, D. Gerten, J. Heinke, M. Konzmann, and O. Varis
Hydrol. Earth Syst. Sci., 18, 447–461, https://doi.org/10.5194/hess-18-447-2014, https://doi.org/10.5194/hess-18-447-2014, 2014
P. Dass, C. Müller, V. Brovkin, and W. Cramer
Earth Syst. Dynam., 4, 409–424, https://doi.org/10.5194/esd-4-409-2013, https://doi.org/10.5194/esd-4-409-2013, 2013
J. Heinke, S. Ostberg, S. Schaphoff, K. Frieler, C. Müller, D. Gerten, M. Meinshausen, and W. Lucht
Geosci. Model Dev., 6, 1689–1703, https://doi.org/10.5194/gmd-6-1689-2013, https://doi.org/10.5194/gmd-6-1689-2013, 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
S. Shukla, J. Sheffield, E. F. Wood, and D. P. Lettenmaier
Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, https://doi.org/10.5194/hess-17-2781-2013, 2013
S. Hagemann, C. Chen, D. B. Clark, S. Folwell, S. N. Gosling, I. Haddeland, N. Hanasaki, J. Heinke, F. Ludwig, F. Voss, and A. J. Wiltshire
Earth Syst. Dynam., 4, 129–144, https://doi.org/10.5194/esd-4-129-2013, https://doi.org/10.5194/esd-4-129-2013, 2013
Related subject area
Biogeosciences
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Exploring the Potential of History Matching for Land Surface Model Calibration
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Implementing a coral reef CaCO3 production module in the iLOVECLIM climate model
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
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This study introduces a new scheme to simulate surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal response to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2024-464, https://doi.org/10.5194/egusphere-2024-464, 2024
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Our study employs Long Short-Term Memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlocking the secrets of vegetation phenology responses to climate change with deep learning techniques.
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems as the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
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In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
EGUsphere, https://doi.org/10.5194/egusphere-2023-1162, https://doi.org/10.5194/egusphere-2023-1162, 2023
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon-climate model and its validation in comparison to existing modern data.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
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Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
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Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
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Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
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Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
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Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
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Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
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In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
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Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
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Short summary
We present and describe the Global Gridded Crop Model Intercomparison (GGCMI) project, an ongoing international effort to 1) validate global models of crop productivity, 2) improve models through detailed analysis of processes, and 3) assess the impacts of climate change on agriculture and food security. We present analysis of data inputs for the project, detailed protocols for conducting and evaluating simulation outputs, and example results.
We present and describe the Global Gridded Crop Model Intercomparison (GGCMI) project, an...
