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latent and sensible heat fluxes from plant canopies and thereby (among other effects) reduces the formation of low-level clouds ( Sellers et al. 1996 ; Doutriaux-Boucher et al. 2009 ; de Arellano et al. 2012 ). Moreover, vegetation cover changes and increasing leaf area due to CO 2 fertilization can lead to surface albedo modifications and changes in dust mobilization ( Zickfeld et al. 2011 ; Andrews et al. 2012 ). All of these processes change the radiative balance within the atmosphere and
latent and sensible heat fluxes from plant canopies and thereby (among other effects) reduces the formation of low-level clouds ( Sellers et al. 1996 ; Doutriaux-Boucher et al. 2009 ; de Arellano et al. 2012 ). Moreover, vegetation cover changes and increasing leaf area due to CO 2 fertilization can lead to surface albedo modifications and changes in dust mobilization ( Zickfeld et al. 2011 ; Andrews et al. 2012 ). All of these processes change the radiative balance within the atmosphere and
control simulation, in the differently coupled simulations are represented as follows: radiatively coupled biogeochemically coupled fully coupled which serve to define the carbon–concentration ( ) and carbon–climate ( ) feedback parameters and assume an approximately linear response of the globally integrated surface–atmosphere CO 2 flux in terms of global mean temperature and CO 2 concentration change. The control simulation has no anthropogenic emissions and a specified atmospheric CO 2
control simulation, in the differently coupled simulations are represented as follows: radiatively coupled biogeochemically coupled fully coupled which serve to define the carbon–concentration ( ) and carbon–climate ( ) feedback parameters and assume an approximately linear response of the globally integrated surface–atmosphere CO 2 flux in terms of global mean temperature and CO 2 concentration change. The control simulation has no anthropogenic emissions and a specified atmospheric CO 2
in a manner similar to (2) , with where dh ′/ dt , the rate of change of the heat content of the system (mainly storage in the ocean), is equal to the change R ′ in the radiative flux across the top of the atmosphere. The radiative flux change R ′ reflects the balance between the radiative forcing f ( C ′, Y ′) due to changes in the CO 2 concentration C and other quantities Y that affect the radiative flux (such as non-CO 2 greenhouse gases, aerosols, land use changes, and changes in
in a manner similar to (2) , with where dh ′/ dt , the rate of change of the heat content of the system (mainly storage in the ocean), is equal to the change R ′ in the radiative flux across the top of the atmosphere. The radiative flux change R ′ reflects the balance between the radiative forcing f ( C ′, Y ′) due to changes in the CO 2 concentration C and other quantities Y that affect the radiative flux (such as non-CO 2 greenhouse gases, aerosols, land use changes, and changes in
decade ( Fig. 5i ) with being an important source of variability in the first decade ( Fig. 5l ), strongly suggesting that anthropogenic land-use change is an important early source of variance in tropical land region carbon fluxes. It is interesting to note that the progression of the RCP scenario radiative forcing from low to high is not repeated in the degree of carbon uptake in the tropics ( Fig. 5i ), with RCP8.5 appearing to have a lower carbon uptake than RCP4.5 and RCP6.0. 5. Discussion We
decade ( Fig. 5i ) with being an important source of variability in the first decade ( Fig. 5l ), strongly suggesting that anthropogenic land-use change is an important early source of variance in tropical land region carbon fluxes. It is interesting to note that the progression of the RCP scenario radiative forcing from low to high is not repeated in the degree of carbon uptake in the tropics ( Fig. 5i ), with RCP8.5 appearing to have a lower carbon uptake than RCP4.5 and RCP6.0. 5. Discussion We
, 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
, 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
factor as discussed above. The choice of those particular values was also based on a number of different quantitative methods using a variety of models and statistical methods, considering uncertainties in radiative forcing, climate feedbacks, ocean heat uptake, and the carbon cycle derived from models and observations (see Knutti et al. 2008 , and references therein). The World Climate Research Programme’s phase 5 of the CMIP (CMIP5) is the main resource for the IPCC AR5 assessment of climate
factor as discussed above. The choice of those particular values was also based on a number of different quantitative methods using a variety of models and statistical methods, considering uncertainties in radiative forcing, climate feedbacks, ocean heat uptake, and the carbon cycle derived from models and observations (see Knutti et al. 2008 , and references therein). The World Climate Research Programme’s phase 5 of the CMIP (CMIP5) is the main resource for the IPCC AR5 assessment of climate
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
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
observations, new estimates of historical CO 2 emissions, and the newly available CMIP5 output. 2. Models and data We use output from prescribed CO 2 simulations from 24 CMIP5 models in this study. Of these, 15 are ESMs providing the output necessary to directly diagnose CO 2 emissions and hence calculate TCRE (see Table 1 for further details). Monthly-mean atmosphere–ocean and atmosphere–land biosphere carbon fluxes were globally integrated with appropriate area weighting and summed over time to
observations, new estimates of historical CO 2 emissions, and the newly available CMIP5 output. 2. Models and data We use output from prescribed CO 2 simulations from 24 CMIP5 models in this study. Of these, 15 are ESMs providing the output necessary to directly diagnose CO 2 emissions and hence calculate TCRE (see Table 1 for further details). Monthly-mean atmosphere–ocean and atmosphere–land biosphere carbon fluxes were globally integrated with appropriate area weighting and summed over time to
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
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
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
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
