Articles | Volume 12, issue 2
https://doi.org/10.5194/gmd-12-677-2019
https://doi.org/10.5194/gmd-12-677-2019
Model description paper
 | 
15 Feb 2019
Model description paper |  | 15 Feb 2019

GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems

Katherine Calvin, Pralit Patel, Leon Clarke, Ghassem Asrar, Ben Bond-Lamberty, Ryna Yiyun Cui, Alan Di Vittorio, Kalyn Dorheim, Jae Edmonds, Corinne Hartin, Mohamad Hejazi, Russell Horowitz, Gokul Iyer, Page Kyle, Sonny Kim, Robert Link, Haewon McJeon, Steven J. Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise

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Cited articles

Akimoto, K., Sano, F., Homma, T., Oda, J., Nagashima, M., and Kii, M.: Estimates of GHG emission reduction potential by country, sector, and cost, Energ. Policy, 38, 3384–3393, 2010. 
Bauer, N., Rose, S. K., Fujimori, S., van Vuuren, D. P., Weyant, J., Wise, M., Cui, Y., Daioglou, V., Gidden, M. J., Kato, E., Kitous, A., Leblanc, F., Sands, R., Sano, F., Strefler, J., Tsutsui, J., Bibas, R., Fricko, O., Hasegawa, T., Klein, D., Kurosawa, A., Mima, S., and Muratori, M.: Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison, Clim. Change, https://doi.org/10.1007/s10584-018-2226-y, online first, 2018. 
Bond-Lamberty, B., Dorheim, K. R., Cui, Y., Horowitz, R. L., Snyder, A. C., Calvin, K. V., and Feng, L.: The gcamdata package: preparation, synthesis, and tracking of input data for the GCAM integrated human-earth systems model, J. Open Res. Softw., accepted, 2018. 
Bond, T. C., Bhardwaj, E., Dong, R., Jogani, R., Jung, S., Roden, C., Streets, D. G., and Trautmann, N. M.: Historical emissions of black and organic carbon aerosol from energy-related combustion, 1850–2000, Global Biogeochem. Cy., 21, GB2018, https://doi.org/10.1029/2006GB002840, 2007. 
Bosetti, V., Massetti, E., and Tavoni, M.: The Witch Model: Structure, Baseline, Solutions, 12064, 1–56, 2007. 
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Short summary
This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM examines the future evolution of these systems through the end of the 21st century. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions.