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      Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks

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          Abstract

          In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO 2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO 2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO 2 emissions, models simulate a large spread in atmospheric CO 2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO 2 (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m −2) when driven by CO 2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO 2, with the present-day biases reasonably well correlated with future atmospheric concentrations’ departure from the prescribed concentration. The uncertainty in CO 2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO 2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO 2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks.

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          An Overview of CMIP5 and the Experiment Design

          The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
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            Global response of terrestrial ecosystem structure and function to CO2and climate change: results from six dynamic global vegetation models

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              The use of the multi-model ensemble in probabilistic climate projections.

              Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.
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                Author and article information

                Journal
                Journal of Climate
                J. Climate
                American Meteorological Society
                0894-8755
                1520-0442
                January 2014
                January 2014
                : 27
                : 2
                : 511-526
                Affiliations
                [1 ]* College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
                [2 ] Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany, and School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia
                [3 ] Canadian Centre for Climate Modelling and Analysis, Environment Canada, University of Victoria, Victoria, British Columbia, Canada
                [4 ] Met Office Hadley Centre, Exeter, United Kingdom
                [5 ] Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
                Article
                10.1175/JCLI-D-12-00579.1
                09a49d14-b686-4282-91a1-86ed48a19a64
                © 2014
                History

                Molecular medicine,Neurosciences
                Molecular medicine, Neurosciences

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