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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

–climate feedback; likewise, it is possible to derive the carbon–concentration feedback by taking the difference from the fully coupled simulation. Gregory et al. (2009) found that the accumulated carbon fluxes simulated in the BGC and RAD experiments do not add up to the carbon flux occurring in the COU simulation in the third climate configuration of the Met Office Unified Model in lower resolution with carbon cycle (HadCM3LC). A similar result is found by Zickfeld et al. (2011) , who used an EMIC [the

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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

–atmosphere exchange of carbon, it is imperative to attribute the variation in these fluxes to their component sources. “GCM variability” originates from an incomplete understanding of physical processes including both climate and ecosystem processes, involved in air–surface carbon exchange and from the limitation of GCMs to represent known behavior. “Scenario variability” arises from uncertainty in future human activity; socioeconomic storylines of population and technology growth are produced by integrated

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

thus an important factor controlling future CO 2 levels in the atmosphere. Fluxes of CO 2 through the air–sea interface are controlled by winds and by differences in the partial pressure of CO 2 ( p CO 2 ) in the surface ocean compared with the overlying atmosphere (e.g., Takahashi et al. 2002 ). Since geographical variations of atmospheric p CO 2 are relatively small ( Conway et al. 1994 ; Takahashi et al. 2002 ), temporal and spatial variations of Southern Ocean p CO 2 are key to

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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

–climate feedback parameter is generally positive from the atmosphere's perspective as higher temperatures promote a flux of carbon from the land and ocean into the atmosphere. The positive carbon–climate feedback acts to reduce the capacity of the land and ocean to take up carbon resulting in a larger fraction of anthropogenic CO 2 emissions remaining in the atmosphere as temperatures warm. The first Coupled Carbon Cycle Climate Model Intercomparison Project (C 4 MIP) found that this positive carbon

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

1. Introduction The global carbon cycle consists of the combined interactions among a series of carbon reservoirs in the earth system (such as CO 2 in the atmosphere, soil organic carbon and vegetation, and carbonate and phytoplankton in the ocean) and all the fluxes and feedbacks that regulate dynamics in the sizes of these reservoirs. Most of the sensitivity and uncertainty in coupled carbon–climate projections lie in the terrestrial (rather than oceanic) carbon cycle (e.g., Zeng et al

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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

roughness. The second, the biogeochemical pathway, takes into account alterations of the atmospheric concentrations of greenhouse gases (GHGs) such as CO 2 , CH 4 , and N 2 O in response to changes in the land–atmosphere fluxes of these trace gases ( Arora and Boer 2010 ; Canadell et al. 2007 ; Houghton 2003 ; House et al. 2002 ; Pongratz et al. 2009 ; Shevliakova et al. 2009 ). Numerous biogeophysical and biogeochemical processes are parameterized in the land surface schemes of atmospheric general

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G. J. Boer and V. K. Arora

1. Introduction The distribution of carbon in the atmosphere, land, and ocean is changing as a consequence of the anthropogenic emission of CO 2 . Biogeochemical processes in the carbon cycle are directly affected by an increase in atmospheric CO 2 , which alters the flux of carbon between the atmosphere and the underlying surface. An increase in atmospheric CO 2 also affects the energy budget, resulting in warmer temperatures and other changes in climate that, in turn, affect the carbon

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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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Spencer Liddicoat, Chris Jones, and Eddy Robertson

can be found in Jones et al. (2011) . The atmosphere model is composed of 38 levels, with a vertical extent of 39 km, and horizontal resolution of 1.875° (east–west) × 1.25° (north–south). The Met Office Surface Exchange Scheme, version 2 (MOSESII; Essery et al. 2001 ) calculates fluxes of heat, moisture, and momentum between the atmosphere and the land surface. The net primary productivity of vegetation covering the land surface is determined by MOSESII as a function of atmospheric CO 2 , light

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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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