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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

circulation models (AGCMs). These schemes simulate the exchange of heat, moisture, and CO 2 between the land surface and the atmosphere (e.g., Bonan 2008 ; Dickinson et al. 1993 ; Sellers et al. 1997 ). LULCC was previously shown to result in seasonal changes in temperature, precipitation patterns, snow cover in high-latitude regions, and atmospheric dynamics (e.g., Bala et al. 2006 ; Chase et al. 2000 ; Claussen et al. 2004 ; Feddema et al. 2005 ). The Land-Use and Climate, Identification of

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Charles D. Koven, William J. Riley, and Alex Stern

( Schneider von Deimling et al. 2012 ; Harden et al. 2012 ; Burke et al. 2012 ). Table 1. List of models used in this analysis, the modeling groups that developed them, model attributes, and references. The model attributes listed here are relevant to soil physics at high latitudes, including whether the model includes a multilayer snow model, whether the snow acts to insulate between the soil and atmosphere, the inclusion of soil water latent heat and differing frozen- and unfrozen-soil thermal

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

basis for improving estimates of vegetation and soil carbon, natural exchanges of CO 2 , and net historic shifts of carbon between the biosphere and the atmosphere ( Gibbs 2006 ). To reflect the changes in land cover over time, Gibbs (2006) updated the Olson et al. (1985) database to a more contemporary land-cover representation using the Global Land Cover 2000 database ( European Commission, Joint Research Centre 2003 ) to estimate biomass carbon in living vegetation on a global scale. Data

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

between CO 2 emissions and CO 2 concentration. A lack of understanding and observations of physical feedbacks reflected in model spread is indeed the main source of uncertainty in long-term climate projections (e.g., Hawkins and Sutton 2009 ). While the initial Planck response to an increase in atmospheric CO 2 is known, the cascade of feedbacks arising from the warming-induced changes in water vapor, lapse rate, clouds, snow, and ice is far from being completely understood ( Bony et al. 2006

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Eleanor J. Burke, Chris D. Jones, and Charles D. Koven

temperatures ( Koven et al. 2013 ). However, the snow scheme within JULES is an updated multilayer snow scheme that increases the snow insulation effect and provides a more realistic estimate of permafrost extent ( Burke et al. 2013 ). b. CMIP5 global climate models The data analyzed here were obtained from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel data archive. These CMIP5 global climate models support the Intergovernmental Panel on Climate Change Fifth Assessment Report

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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

control on the terrestrial and ocean carbon exchange with the atmosphere ( Houghton 2000 ; Schaefer et al. 2002 ); therefore, we also provide an evaluation of the physical variables. The main physical factors controlling the land carbon balance are the surface temperature and precipitation ( Piao et al. 2009 ), but also the cloud cover through its control on incoming radiation is important for the land carbon balance. However, we decided to consider only the two most important variables influencing

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