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

-estimate projections and uncertainty ranges for emission scenarios, there are two major sources of uncertainty that need to be taken into account. The first relates to physical processes and feedbacks, and the uncertainty they induce on climate response for a given greenhouse gas (GHG) concentration and aerosol forcing in terms of the global-mean temperature response, and regional climate change; while the second relates to carbon cycle processes and feedbacks, with the associated uncertainty on the relationship

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Spencer Liddicoat, Chris Jones, and Eddy Robertson

, from a business-as-usual high-emissions scenario to one of aggressive mitigation with early and considerable cuts in emissions. The use of concentration pathways, rather than emissions scenarios, represents a departure from CMIP phase 3 (CMIP3) and other notable projects such as the Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP; Friedlingstein et al. 2006 ). One advantage of this approach is that a spread of radiative forcing pathways is guaranteed, uncertainty in the

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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

state means that the comparison of the behavior of the coupled carbon–climate system across models is more straightforwardly investigated for a common scenario. The fifth phase of the Coupled Model Intercomparison Project (CMIP5; ) ( Taylor et al. 2012 ) provides a common framework for comparing and assessing Earth system processes in the context of climate simulations. A 140-yr-long simulation in which atmospheric CO 2 concentration increases at a

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

was obtained from the Joint UK Land Environment Simulator (JULES) land surface scheme ( Best et al. 2011 ) driven by the Water and Global Change (WATCH) forcing data ( Weedon et al. 2011 ) at a 2° resolution ( Burke et al. 2013 ). Using this reference model allows us to remove the considerable uncertainty associated with initial permafrost distributions across the CMIP5 models, while still allowing us to sample model uncertainty arising from other aspects of the CMIP5 ensemble, such as the climate

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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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Nathan P. Gillett, Vivek K. Arora, Damon Matthews, and Myles R. Allen

; Matthews et al. 2009 ). We note, however, that the transient climate response to cumulative carbon emissions (TCRE) is not directly applicable to emissions of non-CO 2 greenhouse gases and other forcing agents. Matthews et al. (2009) propose that the TCRE of a model is defined as the ratio of warming to cumulative CO 2 emissions in a simulation with a prescribed 1% yr −1 increase in CO 2 at the time when CO 2 reaches double its preindustrial concentration, paralleling the definition of 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

. 2011 ), RCP6.0 ( Masui et al. 2011 ), RCP4.5 ( Thomson et al. 2011 ), and RCP2.6 ( van Vuuren et al. 2011a )—lead to an approximate increase in global radiative forcing by the year 2100 of 8.5, 6.0, 4.5, and 2.6 W m −2 , respectively. The scenarios are sufficiently separated in terms of the radiative forcing pathways to provide distinguishable climate results at the global scale ( Moss et al. 2010 ). The RCP scenarios have a harmonized historical period, assumptions for carbon emissions and

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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

.5, RCP6.0, and RCP8.5. These future scenarios include a CO 2 concentration pathway computed to be consistent with anthropogenic carbon emissions as generated by four integrated assessment models (IAMs). The RCPs are labeled according to the approximate global radiative-forcing level at 2100 with CO 2 concentrations reaching 421, 538, 670, and 936 ppm, respectively ( Fig. 1a ). The RCP2.6 CO 2 pathway peaks at a concentration of 443 ppm at 2050 before declining in the latter half of the century and

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

interest, the slopes and intercepts should be assessed as well. In contrast, goodness of fit at the global scale referred to the absolute difference in the global vegetation carbon between ESMs and the observations. At the grid scale, it is hard to compare plant functional types (PFTs) and climate forcing between ESMs and observations, as well as between ESMs, because each modeling center adopts its own grid configuration and representation of PFTs, which can differ considerably from one another

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