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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

-use changes on climate, several CMIP5 modeling groups performed additional LUCID–CMIP5 simulations without anthropogenic land-use changes from 2006 to 2100. The differences between simulations with and without land-use changes reveal climatic effects of LULCC on global and regional scales. In this paper, we examine the biogeophysical effects and changes in the land carbon storage due to LULCC, focusing on two RCP simulations driven by prescribed CO 2 concentrations. These simulations allow us to quantify

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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

concentration pathway ( Jones et al. 2006 ; Plattner et al. 2008 ). This method has become widespread and was recommended by Hibbard et al. (2007) as the experimental design for CMIP5 and has subsequently been used to present compatible emissions from the CMIP5 multimodel ensemble ( Jones et al. 2013 ). The natural uptake of carbon by land and ocean biospheres is sensitive to both changes in climate and the concentration of atmospheric CO 2 and the balance between these large but offsetting effects on

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

and dynamics in regional fire frequency]. Explicit land use change (LUC) projections are provided along with future CO 2 emission projections and are represented in the region-specific simulations through different land use classifications, parameter settings, allocation rules, and biogeographical patterns in CMIP5. Furthermore, CMIP5 includes non-CO 2 anthropogenic emissions (e.g., sulfur and black and organic carbon) and more diverse coupled carbon cycle experiments than C 4 MIP [e

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

of these models, which can then serve to inform future development ( Luo et al. 2012 ). By doing this in the context of an intercomparison of future predictions, we seek to analyze how model differences that can be seen in the current climate affect the future response. A number of authors have developed high-latitude-specific models of the exchange of energy and water to study the behavior of soil freeze and thaw processes. These models were initially developed for local and regional studies

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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

the a posteriori surface CO 2 fluxes inferred from monthly atmospheric CO 2 observations at stations from the GLOBALVIEW dataset after accounting for the effects of atmospheric transport on a prescribed a priori surface flux, which is corrected during the atmospheric inversion ( Gurney et al. 2003 ). In other words, the goal of the atmospheric inversion process is to find the most likely combination of regional surface net carbon fluxes that best matches observed CO 2 within their error, given

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Spencer Liddicoat, Chris Jones, and Eddy Robertson

agriculture ( Fig. 5b ). These effects combine to increase vegetation carbon throughout, although the rate of increase decreases over time, contributing to the reduction in land uptake. Soil carbon increases until about 2250, when it plateaus ( Fig. 2f ), also limiting F AL . The cumulative total of E FF from 2006 to 2100 is 873 GtC, compared with the IAM equivalent of 785 GtC. From 2101 to 2299, E FF totals 459 GtC, greater than IAM equivalent (272 GtC), as the rate of increase of E FF slightly

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G. J. Boer and V. K. Arora

(all models also perform a fully coupled simulation, of course). More recent analyses also make use of results from radiatively coupled simulations where CO 2 radiative effects are included, but not biogeochemical effects. Feedbacks may be calculated using full ESMs and/or less comprehensive earth system models of intermediate complexity (EMICs), and the models may be forced by specifying emissions or concentrations of CO 2 and may include forcings from other greenhouse gases, aerosols, etc

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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

1. Introduction It is estimated that, at present, the world’s oceans take up approximately 25% of anthropogenic CO 2 emissions ( Le Quéré et al. 2013 ), thereby reducing the atmospheric CO 2 burden. At the same time, climate change modifies ocean circulation and the physical and chemical properties of seawater, which in turn can alter CO 2 uptake. These CO 2 and climate-driven effects are referred to as carbon–concentration and carbon–climate feedback ( Boer and Arora 2009 ; Gregory et al

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

different residence times, so allocation of NPP to different pools will also affect residence times of vegetation carbon substantially. Therefore, improved representation of regional PFTs and their respective prescribed allocation coefficients and longevity of different biomass pools are critical components for improving ESMs’ performance. The number of PFTs represented in these ESMs varied from 5 to 15 ( Table 1 ), and the combinations within grid cells differed considerably between ESMs (please refer

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

documentation for these ESMs. In general, with typical resolution of 2° × 2°, most of the CMIP5 models represent the effects of eddies using eddy parameterizations (e.g., Gent and McWilliams 1990 ). However, as summarized by Gnanadesikan et al. (2006) , GFDL-ESM2M uses the skew flux approach of Griffies (1998) , in which the quasi-Stokes streamfunction is computed ( Ferrari et al. 2010 ). These models also differ in the bathymetry dataset they used. For instance, BCC_CSM1.1 used the 5-min digital

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