1. Introduction
Earth system models (ESMs) incorporate terrestrial and ocean carbon cycle processes into coupled atmosphere–ocean general circulation models (AOGCMs) in order to represent the interactions between the carbon cycle and the physical climate system. Changes in the physical climate affect the exchange of CO2 between the atmosphere and the underlying land and ocean, and the resulting changes in atmospheric concentration of CO2 in turn affect the physical climate. Aspects of the behavior of the carbon cycle and its interaction with the physical climate system are characterized in terms of carbon–concentration and carbon–climate feedback parameters (Friedlingstein et al. 2006; Boer and Arora 2009; Roy et al. 2011). Feedback parameters can be calculated for global averages, separately over land and ocean, over specific regions or for individual grid cells in order to investigate their geographical distribution as in Boer and Arora (2010). The carbon–concentration feedback parameter is a measure of the response of the land and ocean carbon pools to changes in atmospheric CO2 concentration. It is a negative feedback from the perspective of the atmosphere, since the higher values of atmospheric CO2 that result from anthropogenic emissions are partially offset by a loss of atmospheric carbon to the underlying land and ocean. The carbon–climate feedback parameter is a measure of the response to changes in temperature and other climate variables. The carbon–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 CO2 emissions remaining in the atmosphere as temperatures warm. The first Coupled Carbon Cycle Climate Model Intercomparison Project (C4MIP) found that this positive carbon–climate feedback varied significantly across ESMs due mainly to the differences in the behavior of terrestrial carbon cycle components (Friedlingstein et al. 2006).
Both carbon–climate and, in particular, carbon–concentration feedback parameters have been found to be sensitive to the emission scenario, the state of the system, and the approach used to calculate them (Boer and Arora 2009, 2010; Plattner et al. 2008; Gregory et al. 2009; Zickfeld et al. 2011). As a result, values of feedback parameters from one scenario cannot be used, in a quantitative way, to project carbon cycle behavior for a different emission scenario. The geographical patterns of the feedback parameters are, however, found to be reasonably robust across different emissions scenarios (Boer and Arora 2010) and the feedback parameters do serve to illustrate and quantify the carbon feedback processes operating in the coupled carbon–climate system. The dependence of the feedback parameters on emission scenario and system 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; http://cmip-pcmdi.llnl.gov/cmip5/forcing.html) (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 CO2 concentration increases at a rate of 1% yr−1 from preindustrial values until concentration quadruples is a standard CMIP experiment that serves to quantify the response to increasing CO2. To isolate feedbacks, additional radiatively and biogeochemically coupled versions of this “1% increasing CO2” experiment are performed. In radiatively coupled simulations increasing atmospheric CO2 affects the climate but not the biogeochemistry, for which the preindustrial value of atmospheric CO2 concentration is prescribed. In the biogeochemically coupled simulation the biogeochemistry responds to the increasing atmospheric CO2 while the radiative forcing remains at preindustrial values. The simulations do not include the confounding effects of changes in land use, non-CO2 greenhouse gases, aerosols, etc., and so provide a controlled experiment with which to compare carbon–climate interactions across models. Results from eight of the comprehensive Earth system models participating in the CMIP5 intercomparison project are analyzed as well as results from an Earth system model of intermediate complexity (EMIC).
2. Feedbacks in the coupled climate–carbon system
a. Direct/instantaneous feedback parameters
Following Boer and Arora (2009, 2010) and the accompanying paper by Boer and Arora (2013, hereafter BA), the changes in atmosphere carbon budgets, from the control simulation, in the differently coupled simulations are represented as follows:
- radiatively coupled
- biogeochemically coupled
- fully coupled
Carbon budget changes for the land component parallel (3) but without the emissions terms as
- radiatively coupled
- biogeochemically coupled
- fully coupled
The feedback parameters 
b. Integrated flux-based feedback parameters
- radiatively coupled
- biogeochemically coupled
- fully coupled
c. Feedback contributions
d. Gain
3. Model descriptions
The primary features of the nine participating models are summarized in Table 1 and brief descriptions of their terrestrial and oceanic carbon cycle components are provided in appendix C. The eight participating comprehensive ESMs, in alphabetical order, are the 1) Beijing Climate Centre (BCC) BCC-CSM1, 2) Canadian Centre for Climate Modeling and Analysis (CCCma) CanESM2, 3) L'Institut Pierre-Simon Laplace (IPSL) IPSL-CM5A-LR, 4) Japan Agency for Marine-Earth Science and Technology (JAMSTEC) MIROC-ESM, 5) Max Planck Institute for Meteorology (MPI) MPI-ESM-LR, 6) National Center for Atmospheric Research (NCAR) CESM1-BGC, 7) Norwegian Climate Centre (NCC) NorESM-ME, and 8) Met Office (UKMO) HadGEM2-ES. The ninth participating model, the University of Victoria (UVic) UVic ESCM 2.9, is an EMIC. The land surface scheme and carbon cycle component in the CESM1-BGC and NorESM-ME models is the community land model (CLM4) (Lawrence et al. 2011) which includes a representation of the nitrogen cycle and its coupling to the terrestrial carbon budget. None of the other participating models includes coupling of terrestrial carbon and nitrogen cycles.
Primary features of the physical atmosphere and ocean components, and land and ocean carbon cycle components of the nine participating models in this study.
4. Results
a. Surface CO2 fluxes and temperature change
Figure 1 displays the specified atmospheric CO2 concentration and the model mean and the intermodel range for simulated temperature change and atmosphere–land and atmosphere–ocean CO2 flux changes (after accounting for the control run drift) and their cumulative values for the fully, radiatively, and biogeochemically coupled simulations. Figure 2 displays the cumulative fluxes for the individual models. In Fig. 1b, the model mean temperature change at the end of the simulation in the fully coupled case (
(a) Atmospheric CO2 concentration (ppm) used in the 1% increasing CO2 simulations. (b) Model mean values and the range across the nine participating models for simulated temperature change, compared to the control simulation, (c) atmosphere–land and (d) atmosphere–ocean CO2 fluxes, and (e),(f) their cumulative values, from the fully, radiatively, and biogeochemically coupled simulations.
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
(a)–(c) Simulated temperature change, (d)–(f) cumulative atmosphere–land CO2 flux, and (g)–(i) cumulative atmosphere–ocean CO2 flux from the fully, radiatively, and biogeochemically coupled 1% CO2 increase simulations for the nine participating models. Note that in (d)–(f) the lines corresponding to the NorESM-ME and CESM1-BGC models essentially overlay each other.
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
The modest increase in 
In the middle row of Fig. 1 the CO2 flux from atmosphere to land and ocean in the biogeochemically coupled simulation (green lines) first increases and then stays between 5 and 7 Pg C yr−1 (Figs. 1c,d). The carbon gains over land are a consequence of the CO2 fertilization effect, which leads to increased gross primary productivity as well as the increase in the fractional coverage of vegetation (in models that model competition between PFTs). A higher concentration of atmospheric CO2 increases the difference in CO2 partial pressure between the atmosphere and the ocean, thereby driving the flux of CO2 into the ocean. Carbon is lost to the atmosphere from both land and ocean in the radiatively coupled simulation. Over land temperature increase promotes increased heterotrophic respiration per unit biomass as well as decreased globally averaged net primary productivity (NPP) (not shown). Regionally, however, temperature increase is expected to enhance mid- to high-latitude primary production (Qian et al. 2010), so the reduction in global NPP is expected to come from the reduction in the tropics. Over the ocean, CO2 loss is associated with warmer temperatures, which reduce CO2 solubility (Goodwin and Lenton 2009).
In Fig. 2, NorESM-ME and CESM1-BGC behave somewhat differently than the other models. Over land, they give up the lowest amount of carbon in response to warming in the radiatively coupled simulation (
b. Cumulative emissions
Figure 3 displays atmospheric carbon budget components in Eqs. (7) and (10) using results from the fully coupled simulation. The results are arranged in descending order according to the models' cumulative emissions. The change in atmospheric carbon burden 
Components of the atmospheric carbon budget of the participating nine models based on (a) Eq. (7) and (b) Eq. (10) using results from the fully coupled 1% yr−1 increasing CO2 simulation. The terms are based on cumulative fluxes at the time of CO2 quadrupling 140 yr after the start of the simulation. The models are arranged in a descending order based on their cumulative emissions values. In panel (a) carbon uptake by land and ocean components and diagnosed cumulative emissions and in panel (b) the airborne fraction and fraction of emissions taken up by land and ocean are also noted for individual models.
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
c. Feedback parameters
1) Carbon–concentration feedback parameter
Figure 4 compares the atmosphere, land, and ocean carbon–concentration feedback parameters (
Comparison of the carbon–concentration feedback parameter, calculated using the BA approach, across the nine participating models for the (a) atmosphere, (b) land, and (c) ocean components (
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
For the oceans 
2) Carbon–climate feedback parameter
Figure 5 compares the atmosphere, land, and ocean carbon–climate feedback parameters (
Comparison of carbon–climate feedback parameter, calculated using the BA approach, across the nine participating models for the (a) atmosphere, (b) land, and (c) ocean components (
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
Increasing temperature leads to an increase in ecosystem respiration per unit biomass, but the absolute magnitude of 
The value of 
The first-order temperature control on ocean–atmosphere CO2 flux is via the solubility of CO2 in seawater, but this varies little among models as seen in Fig. 5c. Additional controls from ocean stratification, circulation, and biology are also part of the temperature–CO2 flux feedback and are generally of the same sign (e.g., warmer, more stratified oceans generally have less vertical flux of carbon into the surface layer). Biologically mediated fluxes affect physical transport (e.g., greater upward mixing flux of carbon is accompanied by greater downward flux of biogenic particles) but do not normally change the sign of the air–sea flux response. However, these processes are much more variable among models than the effect of temperature on solubility. For example, one model has a feedback parameter of opposite sign to the others for small temperature perturbations (Fig. 5c). This may also be in part due to weak temperature forcing early on in the radiatively coupled 1% yr−1 increasing CO2 experiment.
3) Integrated flux-based feedback parameters
Figure 6 displays the carbon–concentration (
Comparison of the integrated flux-based carbon–concentration (
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
The behavior of 
The BA and FEA approaches represent the coupled carbon–climate system feedbacks in different ways. In the BA approach, the feedback parameters represent the response of instantaneous fluxes to changes in CO2 concentration and temperature, and negative and positive surface–atmosphere CO2 fluxes lead to negative and positive feedbacks, respectively. The FEA approach represents the integrated response of the system, and negative and positive fluxes do not necessarily result in feedback parameters of the same sign.
Table 2 gives the integrated flux-based values of feedback parameters (
Values of integrated flux-based carbon–concentration 
4) Feedback contributions
The relative contributions of the carbon–concentration and carbon–climate feedbacks to the carbon budget can be quantified following Eqs. (8) and (11) provided the surface–atmosphere flux in the fully coupled simulation can be represented in terms of feedback parameters with 
Contributions of the carbon–concentration and carbon–climate feedbacks to the emissions carbon budget (a) in terms of their absolute magnitudes and (b) as a fraction of cumulative emissions following Eqs. (8) and (11).
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
Figure 7 displays cumulative emissions (
Land and ocean contributions of the carbon–concentration and carbon–climate feedbacks to the atmospheric carbon budget (a) in terms of their absolute magnitudes and (b) also as a fraction of cumulative emissions following Eqs. (9) and (11).
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
Figures 7 and 8 show that, because of their offsetting nature, similar values of cumulative emissions and airborne fractions result even though the strength of feedbacks vary considerably across models. The higher airborne fraction of cumulative emissions in the CanESM2, NorESM-ME, CESM1-BGC, and MIROC-ESM models (0.64–0.71) is associated with their relatively smaller fraction of emissions taken up by land (0.06–0.17), compared to other comprehensive Earth system models. This is related to a weaker CO2 fertilization effect in these models. In the absence of an explicit terrestrial nitrogen cycle, the strength of the CO2 fertilization effect in CanESM2 is “downregulated” based on the response of plants grown in ambient and elevated CO2 following Arora et al. (2009). The CO2 fertilization effect in the NorESM-ME and CESM1-BGC models is constrained by nitrogen limitation. Finally, unlike other models, which use a biogeochemical approach to model terrestrial photosynthesis, the MIROC-ESM uses an empirical approach to model the photosynthetic response to CO2 (Ito and Oikawa 2002), which implicitly includes the response to nutrient limitation.
5) Gain
Gain 
Gain gE [Eq. (13)] and its estimated value based on feedback parameters 
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
5. Summary and conclusions
Results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1 until values quadruple after 140 years are analyzed. In the biogeochemically coupled simulations, all biogeochemical processes are active but the specified increasing CO2 concentration changes are excluded from the model's radiation code. In the radiatively coupled simulations the model's radiation code responds to specified increases in atmospheric CO2 concentration, but the biogeochemistry components see the preindustrial value. These simulations isolate the system's response to changes in temperature and CO2 concentration. In the fully coupled simulation, all processes are active.
Two approaches are used to characterize the behavior of the coupled carbon–climate system in terms of feedback parameters. In the first approach, carbon–climate (
Carbon–concentration feedback is negative from the atmosphere's perspective and quantifies the loss of CO2 to the land and ocean as atmospheric CO2 concentration increases. The carbon–climate feedback parameter is positive from the atmosphere's perspective because both the land and ocean give up carbon in response to temperature increases. In all models, the magnitude of 
The intermodel range in both feedback parameters is larger for land than for the ocean, and on a global scale the differences in carbon feedbacks among ESMs are dominated by the diverse response of the land carbon cycle components in the participating models. This agrees with results from the Coupled Climate Carbon Cycle Model Intercomparison (C4MIP) of Friedlingstein et al. (2006). The physical and chemical processes that determine CO2 uptake in the ocean at the time scales considered here are generally similar across the models. The spread in the integrated flux-based feedback parameters for the ocean component is much smaller in CMIP5 models considered here than in the C4MIP study (Table 2), with the caveat that a different scenario and approach is used here to calculate feedback parameters.
The contribution of carbon–concentration feedback to diagnosed cumulative emissions, for the 1% increasing CO2 specified concentration simulations analyzed here, is about 4.5 times larger than the carbon–climate feedback. Analogous to gain g that quantifies the relative increase in atmospheric CO2 concentration associated with carbon–climate feedback, gain 
The feedback parameters characterize broad features of system behavior, but they are dependent on the state of the system, the forcing scenario, and the approach used to calculate them, implying that flux changes cannot be characterized solely in terms of linear responses of temperature and CO2 concentration changes. Despite this state dependence, however, the feedback parameters provide insight into the behavior of feedbacks operating in the coupled carbon–climate system and provide a useful common framework for comparing models.
Acknowledgments
We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. CDJ acknowledges U.K. government funding and was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). Both CDJ and PF acknowledge the COMBINE EU project. JFT was supported by the Research Council of Norway through the EarthClim (207711/E10) project. Comments on an earlier version of this paper from Greg Flato and Nathan Gillett and two anonymous reviewers are also greatly appreciated.
APPENDIX A
Solving for Feedback Parameters
The set of Eqs. (3) for the direct/instantaneous feedback BA approach and Eq. (5) for the integrated flux-based FEA approach can be solved in three different ways to obtain values of 
- the R-B approach,
- the R-F approach,
- the B-F approach,
These two conditions are not exactly satisfied for the participating models. Figure A1 shows that the calculated values of 
Calculated values of 
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
APPENDIX B
Cumulative Emissions
Figure B1a shows that the cumulative emissions from the fully coupled simulation [
(a) Comparison of diagnosed cumulative emissions from the fully coupled simulation 
Citation: Journal of Climate 26, 15; 10.1175/JCLI-D-12-00494.1
APPENDIX C
Model Descriptions
a. Beijing Climate Centre CSM1
The Beijing Climate Centre (BCC) CSM1.1 is a fully coupled global climate–carbon model including interactive vegetation and global carbon cycle (Wu et al. 2013). The atmospheric component BCC-AGCM2.1 is a global spectral model with a horizontal resolution of T42, approximately 2.81° × 2.81° transformed grid, and 26 levels in a hybrid sigma/pressure vertical coordinate system with the top level at 2.91 hPa. The dynamical core of the model is described in Wu et al. (2008), a precedent version BCC-AGCM2.0 is detailed in Wu et al. (2010). A new deep convective scheme of Wu (2012) is used in BCC-AGCM2.1. The oceanic general circulation model (OGCM) Modular Ocean Model, version 4 (MOM4-L40) uses a tripolar grid of Murray (1996). The horizontal resolution is 1° longitude by ⅓° latitude between 30°S and 30°N and increases to 1° at 60°N and beyond, and there are 40 z levels in the vertical. It adopts some mature parameterization schemes used in MOM4 (Griffies et al. 2005), including Sweby's tracer-based third-order advection scheme, isopycnal tracer mixing and diffusion scheme, Laplace horizontal friction scheme, K-profile parameterization (KPP) vertical mixing scheme, complete convection scheme, overflow scheme of topographic processing of sea bottom boundary/steep slopes, and shortwave penetration schemes based on spatial distribution of chlorophyll concentration.
The terrestrial carbon cycle components are described in Ji et al. (2008) and models biochemical and physiological processes including photosynthesis and respiration of vegetation; allocation of carbohydrate to leaves, stem, and root tissues; carbon loss due to turnover and mortality of vegetation; and CO2 release into atmosphere through soil respiration. The model can treat 15 plant functional types (PFTs) including natural vegetation and crop and a grid cell can contain up to four PFTs.
The biogeochemistry module of MOM4-L40 is based on the protocols of the Ocean Carbon Cycle Model Intercomparison Project–Phase 2 (OCMIP2; http://www.ipsl.jussieu.fr/OCMIP/phase2/), which parameterizes the process of marine biology in terms of geochemical fluxes without explicit representation of the marine ecosystem and food web processes. It includes five prognostic variables: phosphate (PO4), dissolved organic phosphorus (DOP), dissolved oxygen (O2), dissolved inorganic carbon (DIC), and alkalinity (Alk). Export production (EP) is parameterized by restoring phosphate production to a climatological state (implicitly this eliminates possible feedbacks on productivity). In the oceanic component (MOM4_L40) of BCC-CSM1.1, the restoring EP has been replaced with a prognostic scheme following Yamanaka and Tajika (1996). EP in MOM4_L40 is parameterized as a function of phosphate concentration [PO4], 
b. Canadian Centre for Climate Modeling and Analysis CanESM2
CanESM2 has evolved from the first-generation Canadian Earth System Model (CanESM1) (Arora et al. 2009; Christian et al. 2010) of the Canadian Centre for Climate Modeling and Analysis (CCCma) and is described in Arora et al. (2011). The vertical domain of the atmospheric component of CanESM2 (CanAM4) extends to 1 hPa with the thicknesses of the model's 35 layers increasing monotonically with height. The physical ocean component of CanESM2 has 40 levels with approximately 10-m resolution in the upper ocean compared to 29 levels in CanESM1, providing a much improved representation of the euphotic zone. The ocean horizontal resolution is approximately 1.41° (longitude) × 0.94° (latitude) in CanESM2.
The Canadian Model of Ocean Carbon (CMOC), the ocean carbon cycle component of CanESM2, incorporates an inorganic chemistry module (solubility pump) and a nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model (organic and carbonate pumps) for simulating the ocean–atmosphere exchange of CO2 (Zahariev et al. 2008). Ocean chlorophyll (which affects penetrating shortwave radiation and thus subsurface heating) is a “semi-prognostic” variable that evolves with time but is not advected independently of phytoplankton biomass. Terrestrial ecosystem processes are modeled using the Canadian Terrestrial Ecosystem Model (CTEM), which simulates carbon in three live vegetation pools (leaves, stem, and root) and two dead pools (litter and soil organic carbon) for nine PFTs: needleleaf evergreen and deciduous trees, broadleaf evergreen and cold and dry deciduous trees, and C3 and C4 crops and grasses. (Arora and Boer 2010).
c. L'Institut Pierre-Simon Laplace CM5A-LR
The IPSL-CM5A (Dufresne et al. 2013), is the new generation Earth system model developed at L'Institut Pierre-Simon Laplace (IPSL). The atmosphere and land models of IPSL-CM5 are updated versions of those used in IPSL-CM4 (Marti et al. 2010): namely, the Laboratoire de Météorologie Dynamique Model with Zoom Capability (LMDZ) atmospheric general circulation model (Hourdin et al. 2006) and the ORCHIDEE land surface model (Krinner et al. 2005). The atmospheric and land components use the same regular horizontal grid with 96 × 96 points, representing a resolution of 3.6° × 1.8°, while the atmosphere has 39 vertical levels. The oceanic component is NEMOv3.2 (Madec 2008), which includes the Louvain-la-Neuve sea ice model (LIM; Fichefet and Morales Maqueda 1997) and the marine biogeochemistry model PISCES (Aumont and Bopp 2006). The ocean model has a horizontal resolution of 2°–0.5° and 31 vertical levels.
The land carbon component ORCHIDEE (Krinner et al. 2005) simulates, with a daily time step, processes of photosynthesis, carbon allocation, litter decomposition, soil carbon dynamics, maintenance and growth respiration, and phenology for 13 different plant functional types. The ocean carbon component PISCES (Aumont and Bopp 2006) simulates the cycling of carbon, oxygen, and the major nutrients determining phytoplankton growth (phosphate, nitrate, ammonium, iron, and silicic acid). PISCES also includes a simple representation of the marine ecosystem with two phytoplankton and two zooplankton size classes.
d. Japan Agency for Marine-Earth Science and Technology MIROC-ESM
The MIROC-ESM (Watanabe et al. 2011) is based on the Model for Interdisciplinary Research on Climate (MIROC) global climate model (Nozawa et al. 2007), which interactively couples an atmospheric general circulation model (MIROC-AGCM; Watanabe et al. 2008), including an online aerosol component [Spectral Radiation-Transport Model for Aerosols Species (SPRINTARS 5.00); Takemura et al. 2000], an ocean GCM with sea ice component [Center for Climate System Research (CCSR) Ocean Component Model (COCO); Hasumi 2007], and a land surface model [Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO); Takata et al. 2003].
The MIROC-AGCM has a spectral dynamical core and uses a flux-form semi-Lagrangian scheme for the tracer advection.The grid resolution is approximately 2.81° with 80 vertical levels between the surface and about 0.003 hPa. The physical ocean component of MIROC-ESM (COCO 3.4) has longitudinal grid spacing of about 1.4°, while the latitudinal grid intervals gradually vary from 0.5° at the equator to 1.7° near the North/South Pole with 44 levels in the vertical.
MIROC-ESM includes an NPZD type of ocean ecosystem component (Oschlies 2001) and a terrestrial ecosystem component with dynamic vegetation (SEIB-DGVM; Sato et al. 2007). A version of the model that includes an atmospheric chemistry component [Clouds, Hazards, and Aerosols Survey for Earth Researchers (CHASER); Sudo et al. 2002] is called MIROC-ESM-CHEM but is not used here. MIROC-ESM includes an atmospheric chemistry component (CHASER 4.1), an NPZD-type ocean ecosystem component (Oschlies 2001), and a terrestrial ecosystem component with dynamic vegetation (SEIB-DGVM; Sato et al. 2007). The NPZD sufficiently resolves the seasonal variation of oceanic biological activities at a basinwide scale (Kawamiya et al. 2000). The biological primary production and NPZD variables are computed above the euphotic layer, in a nitrogen base. A constant Redfield ratio (C/N = 6.625) is used to estimate the carbon and calcium flow. The sea–air CO2 flux is calculated by multiplying the difference of ocean–atmosphere CO2 partial pressures by the ocean gas solubility. SEIB-DGVM adopts an individual-based simulation scheme that explicitly captures light competition among trees. Vegetation is classified into 13 PFTs, consisting of 11 tree PFTs and 2 grass PFTs. The dynamics of the two soil organic carbon pools (fast and slow decomposing) is based on the Roth-C scheme (Coleman and Jenkinson 1999).
e. Max Planck Institute for Meteorology ESM-LR
The Earth system model developed at the Max Planck Institute for Meteorology in Hamburg, Germany (MPI-ESM; Giorgetta et al. 2012, manuscript submitted to J. Adv. Model. Earth Syst.), consists of a general circulation model for the atmosphere (ECHAM6) (Stevens et al. 2013; Roeckner et al. 2003) at T63 (1.9° × 1.9°) resolution with 47 vertical levels and the oceanic model MPI-OM with a nominal horizontal resolution of approximately 1.5° and 40 vertical layers (Jungclaus et al. 2013, 2006). This grid setup is a low-resolution (LR) version of the model used for centennial-time-scale simulations in CMIP5. Ocean and atmosphere are coupled daily without flux corrections.
The ocean biogeochemistry module HAMOCC5 (Ilyina et al. 2013; Maier-Reimer et al. 2005) simulates inorganic carbon chemistry and uses an extended NPZD-type description of marine biology in which phytoplankton and zooplankton dynamics depend on temperature, solar radiation, and colimiting nutrients. HAMOCC uses one phytoplankton type for primary production but separates two types of planktonic shell materials (opal and calcium carbonate shells, respectively), which are exported from the euphotic zone with different sinking rates. Additionally, formation and dissolution of sediments is simulated in the model. The land surface model of MPI-ESM, JSBACH (Raddatz et al. 2007), simulates fluxes of energy, water, momentum, and CO2 between land and atmosphere. Each land grid cell is divided into tiles covered with up to 12 plant functional types. A module for vegetation dynamics (Brovkin et al. 2009) is based on the assumption that competition between different PFTs is determined by their relative competitiveness expressed in annual net primary productivity (NPP), as well as natural and disturbance-driven mortality (fire and wind disturbance).
f. National Centre for Atmospheric Research CESM1-BGC
Version 1 of the Community Earth System Model (CESM1) is the successor to version 4 of the Community Climate System Model (CCSM4), which is a fully coupled, global climate model consisting of land, atmosphere, ocean, and sea ice components (Gent et al. 2011). The experiments examined in this manuscript use a configuration of CESM1 with its biogeochemistry modules enabled, a configuration that is denoted as CESM1-BGC and documented by K. Lindsay et al. (2012, unpublished manuscript). The marine ecosystem module (J. K. Moore et al. 2012, personal communication) utilizes multiple phytoplankton functional groups and a single zooplankton class. Phytoplankton growth is controlled by temperature, light, and available nutrients (N, P, Si, and Fe). The land surface model, CLM4 (Lawrence et al. 2012), includes a biogeochemical module with coupled carbon–nitrogen dynamics, which is denoted in some places as CLM4CN (Thornton et al. 2007, 2009).
The land and ocean components both include aeolian deposition of nitrogen as a forcing of the nitrogen cycle. In the standard 1% increasing CO2 experiments, this deposition was prescribed with a fixed preindustrial dataset.
g. Norwegian Climate Centre NorESM-ME
The Norwegian Earth System Model (NorESM-ME) is based on the Community Earth System Model (CESM1), which is managed and maintained by the National Center for Atmospheric Research (NCAR), with some modification to the model components. The NorESM-ME adopts the same coupler (CPL7), atmosphere [Community Atmosphere Model, version 4.0 (CAM4)], terrestrial (CLM4), and sea ice [sea ice component version 7 (CICE4)] modules. However, the ocean component is based on the Miami Isopycnic Coordinate Ocean Model (MICOM), which is coupled together with the Hamburg Oceanic Carbon Cycle (HAMOCC) model (Assmann et al. 2010). In addition, the atmospheric chemistry has been modified following Seland et al. (2008).
The HAMOCC ocean carbon cycle model simulates the carbon chemistry based on the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP) protocols. It also implements an NPZD-type ecosystem model with multinutrient limitation for the marine biological production. The gas exchange formulation is based on formulation by Wanninkhof (1992). In addition to biogeophysical processes, the CLM4 model also implements carbon–nitrogen biogeochemistry with prognostic carbon and nitrogen in vegetation, litter, and soil organic matter (Bonan and Levis 2010; Lawrence et al. 2011). Nitrogen deposition for the 1% increasing CO2 simulations used here was held constant at preindustrial values of 19.45 Tg N yr−1. A more detailed description of the carbon cycle components of NorESM is discussed in Tjiputra et al. (2013).
h. Met Office HadGEM2-ES
HadGEM2-ES (Collins et al. 2011) couples interactive ocean biogeochemistry, terrestrial biogeochemistry and dust, interactive atmospheric chemistry, and aerosol components into an update of the physical model HadGEM1 (Johns et al. 2006). The physical model contains a 40-level 1° × 1°, moving to ⅓° at the equator, ocean and a 38-level 1.875° × 1.25° atmosphere (Martin et al. 2011). HadGEM2-ES has been set up and used to perform CMIP5 simulations as described by Jones et al. (2011).
The ocean biogeochemistry uses the Diat-HadOCC model (I. J. Totterdell and P. R. Halloran 2012, personal communication), an update of HadOCC (Palmer and Totterdell 2001), now simulating diatom and nondiatom phytoplankton functional types; a single zooplankton; and cycling of nitrogen, silica, and iron. Diat-HadOCC is coupled to other Earth system components through the model's physics, iron supplied through dust, air–sea exchange of CO2, and oceanic emission of dimethylsulfide.
The terrestrial carbon cycle is represented by the Met Office Surface Exchanges Scheme, version 2 (MOSES2) land surface scheme (Essery et al. 2003), which simulates exchange of water, energy, and carbon between the land surface and the atmosphere, and the TRIFFID dynamic global vegetation model (Cox 2001), which simulates the coverage and competition between five plant functional types (broadleaf tree, needleleaf tree, C3 and C4 grass, and shrub) and four nonvegetated surface types (bare soil, urban, lakes, and land ice). The soil carbon component has been updated based on the four-pool RothC soil carbon model (Jones et al. 2005).
i. University of Victoria ESCM 2.9
The University of Victoria Earth System Climate Model (UVic ESCM) version 2.9 (Eby et al. 2009) consists of a primitive equation 3D OGCM coupled to a dynamic–thermodynamic sea ice model and an energy–moisture balance model of the atmosphere with dynamical feedbacks (Weaver et al. 2001). The land surface and terrestrial vegetation components are represented by a simplified version of the Hadley Centre's MOSES land surface scheme coupled to the dynamic vegetation model TRIFFID (Meissner et al. 2003). Land carbon fluxes are calculated within MOSES and are allocated to vegetation and soil carbon pools (Matthews et al. 2004). Ocean carbon is simulated by means of an OCMIP-type inorganic carbon–cycle model and an NPZD marine ecosystem model (Schmittner et al. 2007). Sediment processes are represented using an oxic-only model of sediment respiration (Archer 1996).
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