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V. Brovkin, L. Boysen, V. K. Arora, J. P. Boisier, P. Cadule, L. Chini, M. Claussen, P. Friedlingstein, V. Gayler, B. J. J. M. van den Hurk, G. C. Hurtt, C. D. Jones, E. Kato, N. de Noblet-Ducoudré, F. Pacifico, J. Pongratz, and M. Weiss

1. Introduction About one-third to one-half of the land surface has been modified by humans ( Ellis 2011 ; Vitousek et al. 1997 ), and the land-use extent is likely to increase in the future to accommodate a growing demand for land ( Carpenter et al. 2006 ). Anthropogenic land-use and land-cover change (LULCC) affects climate through two different pathways. The biogeophysical pathway considers alteration of the physical characteristics of the land surface such as albedo, soil moisture, and

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Spencer Liddicoat, Chris Jones, and Eddy Robertson

transition from IAM-derived emissions to GCM-simulated concentrations having been avoided. However, when a GCM is forced with a concentration pathway, the CO 2 emissions compatible with it can be diagnosed by analyzing the carbon balance of the model ( section 2c ; Jones et al. 2006 ; Matthews 2006 ). Most of the Earth System GCMs (ES-GCMs) participating in AR5 now diagnose land-use emissions from one or more of a range of land-use processes ( Jones et al. 2013 ), allowing fossil fuel emissions to be

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Alan J. Hewitt, Ben B. B. Booth, Chris D. Jones, Eddy S. Robertson, Andy J. Wiltshire, Philip G. Sansom, David B. Stephenson, and Stan Yip

Project (C 4 MIP; Friedlingstein et al. 2006 ) and is now a mainstream component of coordinated climate simulations like phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012 ). Such coupled climate–carbon cycle ESMs simulate the natural exchange of carbon by the land and ocean with the atmosphere and thus provide a predictive link between emissions and atmospheric concentrations of CO 2 . They can be used to compute the emissions required to follow a prescribed

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Chris Jones, Eddy Robertson, Vivek Arora, Pierre Friedlingstein, Elena Shevliakova, Laurent Bopp, Victor Brovkin, Tomohiro Hajima, Etsushi Kato, Michio Kawamiya, Spencer Liddicoat, Keith Lindsay, Christian H. Reick, Caroline Roelandt, Joachim Segschneider, and Jerry Tjiputra

models ( Booth et al. 2012 ). Such coupled climate–carbon cycle models simulate the natural exchange of carbon by the land and ocean with the atmosphere and thus provide a predictive link between emissions and atmospheric concentrations of CO 2 . In emissions-driven simulations such as in C 4 MIP, these models calculate changes in atmospheric CO 2 concentration given a scenario of emissions. They can also be used to compute the emissions required to follow a prescribed concentration pathway ( Jones

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Vivek K. Arora, George J. Boer, Pierre Friedlingstein, Michael Eby, Chris D. Jones, James R. Christian, Gordon Bonan, Laurent Bopp, Victor Brovkin, Patricia Cadule, Tomohiro Hajima, Tatiana Ilyina, Keith Lindsay, Jerry F. Tjiputra, and Tongwen Wu

the biogeochemically coupled simulation the biogeochemistry responds to the increasing atmospheric CO 2 while the radiative forcing remains at preindustrial values. The simulations do not include the confounding effects of changes in land use, non-CO 2 greenhouse gases, aerosols, etc., and so provide a controlled experiment with which to compare carbon–climate interactions across models. Results from eight of the comprehensive Earth system models participating in the CMIP5 intercomparison

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Pu Shao, Xubin Zeng, Koichi Sakaguchi, Russell K. Monson, and Xiaodong Zeng

. 2005 ; Friedlingstein et al. 2006 ; Denman et al. 2007 ; Booth and Jones 2011 ). The importance of the terrestrial component of the carbon cycle to future model projections is widely recognized, in large part because the terrestrial component is so greatly influenced by anthropogenic activities such as restoration from past disturbances and changes in land use or management ( Houghton 2007 ). The interannual and interdecadal variability in the growth rate of atmospheric CO 2 concentration is

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A. Anav, P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. Myneni, and Z. Zhu

1. Introduction Earth system models (ESMs) are complex numerical tools designed to simulate physical, chemical, and biological processes taking place on Earth between the atmosphere, the land, and the ocean. Worldwide, only a few research institutions have developed such models and used them to carry out historical and future simulations in order to project future climate change. ESMs, and numerical models in general, are never perfect. Consequently, before using their results to make future

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Pierre Friedlingstein, Malte Meinshausen, Vivek K. Arora, Chris D. Jones, Alessandro Anav, Spencer K. Liddicoat, and Reto Knutti

global carbon cycle ( Hibbard et al. 2007 ; Taylor et al. 2012 ). Most of the proposed experiments are performed using prescribed globally averaged CO 2 concentration, not CO 2 emissions, allowing participation of both AOGCMs and ESMs. For a given model, the projected climate change is then independent of the strength of its feedbacks associated with the carbon cycle. Concentration–carbon and climate–carbon feedbacks would affect the carbon fluxes between the atmosphere and the underlying land and

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G. J. Boer and V. K. Arora

. Table 1. Studies involving carbon feedbacks. The direct feedback formalism follows the approach of Boer and Arora (2009) , while the time-integrated formalism follows Friedlingstein et al. (2006) . Special simulations are radiatively or biogeochemically coupled. RCP forcings include non-CO 2 greenhouse gas, aerosol, and land use change, volcano, and solar forcing, as opposed to CO 2 -only radiative forcing changes in the remaining cases. The unit 1% yr −1 indicates that the CO 2 concentration

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Lifen Jiang, Yaner Yan, Oleksandra Hararuk, Nathaniel Mikle, Jianyang Xia, Zheng Shi, Jerry Tjiputra, Tongwen Wu, and Yiqi Luo

simulated by CMIP5 ESMs under four representative concentration pathways (RCPs; Jones et al. 2013 ). This spread was dominated by the variability in the projected land carbon changes, which was partly due to the diverse responses of land carbon cycle models to anthropogenic CO 2 increase and climate change and the different representations of land-use change. Finally, Friedlingstein et al. (2014) also showed that the uncertainty in the land carbon cycle projection was responsible for the large

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