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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

compared to the cumulative flux ( Fig. B1b ), except for the BCC-CSM1.1 and HadGEM2-ES models [as also found by Gregory et al. (2009) for the third-generation low-resolution Hadley Centre climate model with carbon cycle (HadCM3LC)]. The overall good agreement in Fig. B1a is the result of and conditions being met for most models indicating that feedback parameters calculated using results from radiatively and biogeochemically coupled simulations transfer well to the fully coupled case

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Jörg Schwinger, Jerry F. Tjiputra, Christoph Heinze, Laurent Bopp, James R. Christian, Marion Gehlen, Tatiana Ilyina, Chris D. Jones, David Salas-Mélia, Joachim Segschneider, Roland Séférian, and Ian Totterdell

model runs, it is possible to estimate the carbon–climate feedback. Gregory et al. (2009) , Tjiputra et al. (2010) , Boer and Arora (2013) , and Arora et al. (2013) employ radiatively coupled simulations (RAD) with constant preindustrial CO 2 concentration prescribed to the land and ocean biogeochemistry modules while the model’s radiation code sees rising atmospheric CO 2 . The change in carbon uptake (actually a loss) from this type of simulation is an alternative estimate of the carbon

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

simplified schematic of temperature dynamics for northern soils ( Fig. 1 ) shows that the soil temperature annual cycle is driven by changes in the radiative forcing and surface heat exchange, such that the amplitude of the seasonal cycle is greatest in the air, decreases across the air–soil interface, and decreases further with depth into the soils following a roughly exponential profile. The active layer in permafrost soils is defined as the maximum depth at which the annual temperature wave causes the

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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

Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) IAM ( Riahi et al. 2011 ) corresponds to a radiative forcing of more than 8.5 W m −2 and a CO 2 concentration of 936 ppm in 2100. It represents the upper 10th percentile of the future scenario range for CO 2 emissions ( Moss et al. 2010 ). In contrast, the RCP2.6 scenario simulated by the Integrated Model to Assess the Global Environment (IMAGE) IAM ( van Vuuren et al. 2011 ) represents pathways in the lower 10th

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ChuanLi Jiang, Sarah T. Gille, Janet Sprintall, and Colm Sweeney

radiative forcing of all direct and indirect agents reaches 8.5 W m −2 near 2100 ( ). We use results from eight climate modeling groups selected because, as of May 2012, they offered the only ESMs that provided surface water p CO 2 output for historical and RCP 8.5 experiments. The eight climate modeling groups are 1) the Beijing Climate Center Climate System Model, version 1.1 (BCC_CSM1.1; Wu et al. 2013 ); 2) the second generation Canadian Earth System Model

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

and dynamic change of plant coverage. We focus on two suites of CMIP5 experiments—concentration-driven historical and representative concentration pathway 4.5 (RCP4.5). The former is also referred to as the twentieth-century simulations from the mid-nineteenth century to near present and is suitable for comparison with observations. The RCP4.5 experiment provides a future projection of climate from 2006 to 2100 based on a mitigation or stabilization scenario in which the total radiative forcing 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

time scales: first, we analyze the long-term trend, which provides information on the model capability to simulate the temporal evolution over the twentieth century given greenhouse gas (GHG) and aerosol radiative forcing. Second, we analyze the interannual variability (IAV) of physical variables as a constraint on the model capability to simulate realistic climate patterns that influence both ocean and continental carbon fluxes ( Rayner et al. 2008 ). Third, we evaluate the modeled seasonal cycle

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