Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 3995–4018, 2020
https://doi.org/10.5194/gmd-13-3995-2020

Special issue: Agricultural Model Intercomparison and Improvement Project...

Geosci. Model Dev., 13, 3995–4018, 2020
https://doi.org/10.5194/gmd-13-3995-2020

Model description paper 03 Sep 2020

Model description paper | 03 Sep 2020

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

James A. Franke et al.

Related authors

The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)
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
Short summary

Related subject area

Climate and Earth system modeling
Model of Early Diagenesis in the Upper Sediment with Adaptable complexity – MEDUSA (v. 2): a time-dependent biogeochemical sediment module for Earth system models, process analysis and teaching
Guy Munhoven
Geosci. Model Dev., 14, 3603–3631, https://doi.org/10.5194/gmd-14-3603-2021,https://doi.org/10.5194/gmd-14-3603-2021, 2021
Short summary
A Markov chain method for weighting climate model ensembles
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021,https://doi.org/10.5194/gmd-14-3539-2021, 2021
Short summary
Building indoor model in PALM-4U: indoor climate, energy demand, and the interaction between buildings and the urban microclimate
Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519, https://doi.org/10.5194/gmd-14-3511-2021,https://doi.org/10.5194/gmd-14-3511-2021, 2021
Short summary
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
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
Short summary
Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021,https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary

Cited articles

Aulakh, M. S. and Malhi, S. S.: Interactions of Nitrogen with Other Nutrients and Water: Effect on Crop Yield and Quality, Nutrient Use Efficiency, Carbon Sequestration, and Environmental Pollution, Adv. Agron., 86, 341–409, https://doi.org/10.1016/S0065-2113(05)86007-9, 2005. a
Blanc, E.: Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models, Agr. Forest Meteorol., 236, 145–161, https://doi.org/10.1016/j.agrformet.2016.12.022, 2017. a, b, c
Blanc, E. and Sultan, B.: Emulating maize yields from global gridded crop models using statistical estimates, Agr. Forest Meteorol., 214-215, 134–147, https://doi.org/10.1016/j.agrformet.2015.08.256, 2015. a, b, c
Castruccio, S., McInerney, D. J., Stein, M. L., Liu Crouch, F., Jacob, R. L., and Moyer, E. J.: Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs, J. Climate, 27, 1829–1844, https://doi.org/10.1175/JCLI-D-13-00099.1, 2014. a, b
Challinor, A., Wheeler, T., Craufurd, P., Slingo, J., and Grimes, D.: Design and optimisation of a large-area process-based model for annual crops, Agr. Forest Meteorol., 124, 99–120, https://doi.org/10.1016/j.agrformet.2004.01.002, 2004. a
Short summary
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.