A simple object-oriented and open source model for scientific and policy analyses of the global carbon cycle – Hector v 0 . 1

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Introduction
Projecting future impacts of anthropogenic perturbations on the climate system relies on understanding the interactions of key earth system processes.To accomplish this, a hierarchy of climate models with differing levels of complexity and resolution are used, ranging from simple energy balance models to fully-coupled atmosphere-oceangeneral circulation models (AOGCMs) (Stocker, 2011).
Simple climate models (SCMs) represent only the most critical global scale earth system processes with low spatial and temporal resolution, e.g., carbon fluxes between the ocean and atmosphere, and respiration and primary production on land.These models are relatively easy to use and understand, and are computationally inexpensive.Most SCMs have a few key features: (1) calculating future concentrations of greenhouse gases (GHGs) from given emissions, (2) calculating global mean radiative forcing from concentrations, (3) converting the radiative forcing to global mean temperature, and (4) modeling the carbon cycle, an essential part of the climate system (e.g., Wigley, 1991;Meinshausen et al., 2011a;Tanaka et al., 2007b;Lenton, 2000).With these capabilities, SCMs play an integral role in policy and scientific research.For example, energy-economic-climate models or Integrated Assessment Models (IAMs) are used to address issues on energy system planning, climate mitigation and stabilization pathways, land-use changes, pollution control, and population policies (Wigley et al., 1996;Edmonds and Smith, 2006;van Vuuren et al., 2011c).AOGCMs are too computationally expensive to use in these analyses.Therefore all IAMs have a simple representation of the global climate system in which emissions data from the IAMs are converted to concentrations and then radiative forcing and global temperature are calculated.
SCMs are also used as emulators of more complex AOGCMs (e.g., Meinshausen et al., 2011a, c;Schlesinger and Jiang, 1990;Challenor, 2012;Ratto et al., 2012).The components of SCMs can be constrained to replicate the overall behavior of the more complex model components.For instance, the climate sensitivity of a SCM can be made equal to that of an AOGCM by altering a single model parameter.One SCM, MAGICC, has been central to the analyses presented in the Intergovernmental Panel on Climate Change (IPCC) reports, emulating a large suite of AOGCMs (Meinshausen et al., 2011a).
Lastly, SCMs are computationally efficient and inexpensive to run, and therefore are used for run multiple simulations of future climate change emissions scenarios, parameter sensitivity experiments, perturbed physics experiments, large ensemble runs, and uncertainty analyses (Senior and Mitchell, 2000;Hoffert et al., 1980;Harvey and Schneider, 1985;Ricciuto et al., 2008;Sriver et al., 2012;Irvine et al., 2012).SCMs are fast enough that multiple scenarios can be simulated, and a wide range of parameter Figures

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Full values can be tested.Specifically, SCMs have been useful in reducing uncertainties in future CO 2 sinks, quantifying parametric uncertainties in sea-level rise, ice-sheet modeling, ocean-heat uptake, and aerosol forcings (Ricciuto et al., 2008;Sriver et al., 2012;Applegate et al., 2012;Urban and Keller, 2009).This study introduces Hector v0.1, object-oriented, simple climate carbon-cycle model.Hector is open source, an important quality given that the scientific community, funding agencies, and journals are increasingly emphasizing transparency and open source (E.P. White, 2013;Heron et al., 2013).With an open source model a large community of scientists can access, use, and enhance it, with the potential for long-term utilization and reproducibility (Ince et al., 2012).
One of the basic questions faced in developing a SCM is how much detail should be represented in the climate system.Our goal is to introduce complexity only where warranted, keeping the representations of the climate system as simple as possible.This results in fewer calculations, faster execution times, and easier analysis and interpretation of results.Sections 2, 3, and 4 describe the structure and components of Hector.Sections 5 and 6 describe the experiments, results and comparison of Hector against other models (MAGICC and CMIP5).

Overall structure and design
Hector is written in C++ and uses an object-oriented design that enforces clean separation between its different parts, which interact via strictly defined interfaces.The separation keeps each software module self-contained, which makes the code easy for users to understand, maintain, and enhance.Entities in the model include a commandline wrapper, the model coupler, various components organized around scientific areas (carbon cycling, radiative forcing, etc.) and visitors responsible for model output.Each of these is discussed below.Figures

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Model coupler
Hector's control flow starts with the coupler, which is responsible for: (1) parsing and routing input data to the model components, (2) tracking how the components depend on each other, (3) passing messages and data between components, (4) providing facilities for logging, time series interpolation, etc., and ( 5) controlling the main model loop as it progresses through time.Any errors thrown by the model are caught by the wrapper, which prints a detailed summary of the error.
Input data are specified in flat text files, and during startup are routed to the correct model component for its initialization.Some of the key initial model conditions are summarized in Tables 1 and 2. For more details of initial model conditions we urge the reader to download Hector v0.1 (https://github.com/JGCRI/hector).Components can send messages to each other during the model run, most often requesting data.The coupler handles message routing (via the capability mechanism, below) and enforces mandatory type checking: e.g., if a component requests mean global temperature in • C but the data are provided in K, an error will be thrown (i.e., execution halts) unless the receiving component can handle this situation.
Visitor patterns are units of code that traverse all model components and handle model output (Martin et al., 1997).Two visitors currently exist: one saves an easilyreadable summary table to an output file, while the other writes a stream of model data (both standard outputs and internal diagnostics).After the model is finished running, this "stream" file can be parsed and summarized by R scripts included with the code (R Development Core Team, 2014).Log files may also be written by any model entity, using facilities provided by the coupler.The full sequence of events during a model run is summarized in Fig. 1.

Components
Model components are submodels that communicate with the coupler.From the coupler's point of view, components are fully defined by their capabilities and Figures

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Full dependencies.At model startup, before the run begins, components inform the coupler of their capabilities, i.e., what data they can provide to the larger model system.The coupler uses this information to route messages between components, such as requests for data.Components register their dependencies, i.e., what data they require in order for their computations.After initialization, but before the model begins to run, the coupler uses this dependency information to determine the order in which components will be called in the main control loop.The model's modular architecture, and the capability/dependency systems described above, allows swapping, enabling and disabling of model components directly via the input without recompiling.For example, this means that a user can test two different ocean submodels and easily compare results without having to rebuild the model.

Time step, spinup, and constraints
The model's fundamental time step is 1 year, although the carbon cycle can operate on a finer resolution when necessary (Sect.2.6.1).When the model is on an integer date (e.g.1997.0) it is considered to be the midpoint of that particular calendar year, in accordance with Representative Concentration Pathway (RCP) data (Meinshausen et al., 2011b).
Like many models, Hector has an optional "spinup" step, in which the model runs to equilibrium in an ahistorical, perturbation-free mode (Pietsch and Hasenauer, 2006).This occurs after model initialization, but before the historical run begins, and ensures that the model is in steady state when it enters the main simulation.During spinup, the coupler repeatedly calls all the model components in their dependency-driven ordering, using an annual time step.Each component signals whether it needs further steps to stabilize, and this process repeats until all components signal that they are complete.
Currently only the model's carbon cycle makes use of spinup.Spinup takes place prior to land use change or industrial emission inputs.The main carbon cycle moves from its initial, user-defined carbon pool values to a steady state in which δC/dt < ε Introduction

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Full for all pools; the convergence criterion ε is user-definable and by default 1 Tg C yr −1 .
From its default values the preindustrial carbon cycle will typically stabilize in 300-400 time steps.The model can be constrained, i.e., matching its output to a user-supplied time series, to allow isolation and testing of different components.Available constraints currently include atmospheric CO 2 , global temperature anomaly, total ocean-atmosphere carbon exchange, total land-atmosphere carbon exchange, and total radiative forcing.Most constraints operate by overwriting model-calculated values with user-supplied time series data during the run.The atmospheric [CO 2 ] constraint operates slightly differently, as the global carbon cycle is subject to a continuous mass-balance check.As a result, when the user supplies a [CO 2 ] record between arbitrary dates and orders the model to match it, the model computes [CO 2 ] at each time step, and any deficit (surplus) in comparison with the constraint [CO 2 ] is drawn from (added to) the deep ocean.The deep ocean holds the largest reservoir of carbon; therefore, small changes in this large pool have a negligible effect on the carbon cycle dynamics.When the model exits the constraint time period, [CO 2 ] again becomes fully prognostic.

Code availability and dependencies
All Hector code is open source and available at https://github.com/JGCRI/hector.The repository includes model code that can be compiled on Mac, Linux, and Windows, inputs files for the four Representative Concentration Pathways (RCP) cases discussed in Sect.4, R scripts to process model output, and documentation.We kept the dependencies as limited as possible, with only the GNU Scientific Library (GSL, Gough, 2009) and the Boost C++ libraries (http://www.boost.org).An optional unit testing build target requires the googletest framework (http://code.google.com/p/googletest).However, this is not needed to compile and run Hector.HTML documentation can be automatically generated from the code using the Doxygen tool (http://www.doxygen.org).All these tools and libraries are free and open source.

Main carbon cycle
In the model's default terrestrial carbon cycle, terrestrial vegetation, detritus, and soil are linked with each other and the atmosphere by first-order differential equations (Fig. 2).Vegetation net primary production is a function of atmospheric [CO 2 ] and temperature.Carbon flows from the vegetation to detritus and then soil, losing fractions to heterotrophic respiration on the way.Land-use emissions are specified as inputs.An "earth" pool debits carbon emitted as anthropogenic emissions, allowing a continual mass-balance check across the entire carbon cycle.More formally, any change in atmospheric carbon, and thus [CO 2 ], occurs as a function of anthropogenic emissions, land-use change emissions, and the atmosphereocean carbon flux.The atmosphere is treated as a single well-mixed box whose rate of change is: where, F A is the anthropogenic emissions, F LC is the land use change emissions and F O and F L are the atmosphere ocean and atmosphere land fluxes.The overall terrestrial carbon balance at time t is the difference between net primary production (NPP) and heterotrophic respiration (RH).This is summed over user-specified n groups (each typically regarded as a latitude band, biome, or -political units), with n ≥ 1: Note that NPP here is assumed to include disturbance effects, for which there is currently no separate term.biome's temperature anomaly T i : (5) These are commonly used formulations: NPP is modified by a the user-specified carbon fertilization parameter, β (Piao et al., 2013).RH changes are controlled by a biome-specific Q 10 value.Biomes can experience temperature changes at rates that differ from the global mean T G , controlled by a user specified temperature factor δ I .
Land carbon pools (vegetation, detritus, and soil) change as a result of NPP, RH, and land-use change fluxes, whose effects are partitioned among these carbon pools.In addition, carbon flows from vegetation to detritus and soil (Fig. 2).Partitioning fractions (f ) control the flux quantities between pools (Table 2).For simplicity Eqs. ( 8)-( 10) omit the time t and biome-specific i notations, but each pool is tracked separately for each biome at each time step: The ocean-atmosphere carbon flux is the sum of the ocean's surface fluxes (currently n = 2, high and low latitude surface box):

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Full The surface fluxes of each individual box are calculated from an ocean chemistry model described in detail by Hartin et al. (2014) based on equations from Zeebe and Wolf-Gladrow, (2001).The flux of CO 2 for each box i is calculated by: Where k is the CO 2 gas-transfer velocity, α is the solubility of CO 2 in water based on salinity, temperature, and pressure, and ∆pCO 2 is the atmosphere-ocean gradient of pCO 2 (Takahashi et al., 2009).At steady state, the cold high latitude surface box (> 55 • , subpolar gyres) acts as a sink of carbon from the atmosphere, while the warm low latitude surface box (< 55 • ) off gases carbon back to the atmosphere.Temperatures of the surface boxes are linearly related to atmospheric global temperatures (see Sect. 4.1), T HL = ∆T − 13 and T LL = ∆T + 7 (Lenton, 2000).The ocean model, modeled after Lenton et al. (2000) and Knox and McElroy (1984), circulate carbon through four boxes (two surface, one intermediate depth, one deep), via water mass advection and exchange, simulating a simple thermohaline circulation (Fig. 2).At steady state, approximately 100 Pg of carbon are transferred from the high latitude surface box to the deep box based on the volume of the box and transport in Sv (10 6 m 3 s −1 ) between the boxes.The change in carbon of any box i is given by the fluxes in and out: As the model advances, the carbon values or DIC change in each box.The new DIC values are used within the chemistry submodel to calculate pCO 2 values at the next time step.

Adaptive-time step solver
The fundamental time step in Hector is currently one year, and most model components are solved at this resolution.The carbon cycle, however, can operate on a variable time step, helping to stabilize it under particularly high-emissions scenarios.This Introduction

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Full will also allow future sub-annual applications where desired.The adaptive time step accomplished using the gsl_odeiv2_evolve_apply solver package of GSL 1.16, which attempts many different step sizes to reliably (i.e., with acceptable error) advance the model.Thus all the carbon cycle components handle indeterminate time steps ≤ 1 yr, and can signal the solver if a too-large time step is leading to instability.The solver then re-retries the solution, using a series of smaller steps.From the coupler's point of view, however, the entire model continues to advance in annual increments.
4 Other components

Global atmospheric temperature
Near surface global atmospheric temperature is calculated by: where, user-specified λ is the climate feedback parameter, defined as λ = S /S, where S is the climate sensitivity parameter (3 K) and S is the equilibrium climate sensitivity for a doubling of CO 2 (3.7 W m −2 ) (Knutti and Hegerl, 2008).RF is the total radiative forcing and F H is the ocean heat flux.F H is calculated by a simple expression of the ocean heat uptake efficiency k (W m −2 K −1 ) and the atmospheric temperature change prior to the ocean's removal of heat from the atmosphere (Raper et al., 2002):

Radiative forcing
Radiative forcing is calculated from a series of atmospheric greenhouse gases, aerosols, and pollutants (Eqs.17
Radiative forcing by halocarbons, other gases controlled under the Montreal Protocol, SF 6 , and ozone are calculated via: where α is the radiative efficiency in W m −2 ppbv −1 , and C is the atmospheric concentration.Introduction

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Full where the constants are the ozone sensitivity factors for each of the precursors (Ehhalt et al., 2001).The radiative forcing of tropospheric ozone is calculated from a linear relationship using a radiative efficiency factor (Joos et al., 2001) and a pre-industrial value of ozone of 25 DU (IPCC, 2001):

BC and OC
The radiative forcing from black carbon is a function of the black carbon and organic carbon emissions (eBC and eOC).

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Sulphate aerosols
The radiative forcing from sulphate aerosols is a combination of the direct and indirect forcings (Joos et al., 2001).
The direct forcing by sulphate aerosols is proportional to the anthropogenic sulphur emissions (GgS yr −1 ) divided by the sulphate emissions from 2000.The indirect forcing by sulphate aerosols is a function of the anthropogenic and natural sulphur emissions.Natural sulphur emissions denoted by eSN is equal to 42 000 GgS.A time series of annual mean volcanic stratospheric aerosol forcing (W m −2 ) is supplied from Meinshausen et al. ( 2011b) and is added to the indirect and direct forcing for a total sulphate forcing.

N 2 O and CH 4
The radiative forcing equations for CH 4 and N 2 O (Joos et al., 2001) are a function of the concentrations (ppbv) and their radiative efficiency: The function f accounts for the overlap in CH 4 and N 2 O in their bands is: Note, we are not explicitly calculating concentrations of CH 4 and N 2 O within Hector, instead we have input files of concentrations.

Stratospheric H 2 O from CH 4 oxidation
The radiative forcing from stratospheric H 2 O is a function of the CH 4 concentrations (Tanaka et al., 2007a).The coefficient 0.05 is from Joos et al. (2001) based on the fact that the forcing contribution from stratospheric H 2 O is about 5 % of the total CH 4 forcing (IPCC, 2001).The 0.036 coefficient corresponds to the same coefficient used in the CH 4 radiative forcing equation.

Model experiments and data sources
A critical test of Hector's performance is to compare the major climatic variables calculated in Hector, e.g., atmospheric [CO 2 ], radiative forcing, and atmospheric temperature, to observational records and other models.We run Hector under historical conditions from 1850-2005 and then under all four Representative Concentration Pathways (RCPs) out to 2300 (Moss et al., 2010).The RCPs are plausible future scenarios that are developed to improve our understanding of the coupled human climate system.All necessary emission and concentration inputs are from the four RCPs (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) freely available at http://www.pik-potsdam.de/~mmalte/rcps/ (Meinshausen et al., 2011b;Riahi et al., 2011;van Vuuren et al., 2011a, b, d;Masui et al., 2011;Thomson et al., 2011).Figures

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Full Comparison data is obtained from a series of models.We compared Hector results to MAGICC, a SCM widely used in the scientific and IAM communities, for global variables such as atmospheric CO 2 , radiative forcing, and temperature (e.g., Raper et al., 2001;Wigley, 1995;Meinshausen et al., 2011a).We also compare Hector to a suite of eleven Earth System Models included in the Coupled Model Intercomparison Project (CMIP5) archive (Taylor et al., 2012) (Table 3).All CMIP5 data are converted to yearly global averages from the historical period through the RCPs and their extensions.One SD and the CMIP5 model spread is calculated for each variable.All CMIP5 variables used in this study are from model runs with prescribed atmospheric concentrations, except for comparisons involving atmospheric [CO 2 ] which are from the emissions driven scenario (esmHistorical and esmRCP8.5).The models that run esmRCP8.5 are typically earth system models used to investigate the carbon cycle in further detail.

Historical
A critical test of Hector's performance is how well it compares to historical and present day climate from observations, MAGICC, and a suite of CMIP5 models.We carried out a few statistical tests on Hector (e.g., correlation and root mean square error) which are summarized in Table 4.After spinup is complete in Hector, the atmospheric Compared to observations, MACGICC6, and CMIP5 data from 1850 to 2004, Hector captures the global trends in atmospheric [CO 2 ] (Fig. 3) with correlation coefficients of R > 0.99 and an average root mean square error (RMSE) of 2.6 ppmv (Table 4a).
Hector has the ability to match atmospheric [CO2] records, but we disabled this feature to highlight the full performance of the model.
Historical global atmospheric temperature anomalies (relative to 1850) are compared across Hector, MAGICC6, CMIP5, and observations from HadCRUT4 (Fig. 4).Hector is running without the effects of volcanic forcing, leading to the smoother representation of temperature with time.Atmospheric temperature change from Hector over the period 1850 to 2004 is well correlated to (> 0.8) to observations and models with an average RMSE of 0.12 • C.

Future projections
Within the modeling community, models that best simulate the historical and present day climate are assumed to be credible under future projections.We are confident in Hector's ability to reproduce historical trends and are therefore confident in its ability to simulate future climate changes.We compare Hector to MAGICC and CMIP5 under differing future climate projections.
Figure 5 highlights historical trends in atmospheric [CO 2 ], along with projections of atmospheric [CO 2 ] under esmRCP8.5 from 1850 to 2100.Hector is perfectly correlated with MAGICC and CMIP5 over this period and with a RMSE of 9.2 ppmv (Table 4b).
Hector and MAGICC6 diverge from the CMIP5 median most notably after 2050, but are both still within the low end of the CMIP5 model spread. of carbon over the land as net primary production and respiration change with CO 2 fertilization and temperature effects.We compare Hector to MAGICC6 for changes in radiative forcing under the four RCPs (Fig. 7).Radiative forcing is not an output from the CMIP5 models and therefore we can only compare Hector and MAGICC6.Hector is offset slightly lower compared to MAGICC6, which is expected since atmospheric [CO 2 ] is slightly lower.Over the period 1850 to 2300 Hector is well correlated (1.0) with MAGICC6 with a RMSE of 0.25 W m −2 .We acknowledge that the correlation is lower under the historical period (0.79).This may be due to slight differences in the representation of atmospheric gases, pollutants, and aerosols between the two models.
Figure 8 compares global temperature anomalies from Hector to MAGICC6 and CMIP5 over the four RCPs, from 2005 to 2300.Hector and MAGICC6 are comparable in their temperature change across the four RCPs.However, both are lower than the CMIP5 median under RCP 2.6, 4.5 and 8.5, with the largest discrepancy under high CO 2 emissions in RCP 8.5.Regardless, Hector is still highly correlated (> 0.97) to MAGICC6 and CMIP5 for RCP 8.5, with a RMSE of 0.52 • C compared to CMIP5 (Table 4c).The fluctuations seen in RCP 2.6 within atmospheric [CO 2 ] are also apparent in the atmospheric temperature trends.However, the general trends of temperature change, peaking around 2050 and then slowly declining out to 2300 are captured within Hector.
Another way to visualize model performance is a Taylor diagram (Fig. 9) of global temperature change relative to 1850, from 1850 to 2300 for RCP 8.5.The closer the points are to the reference point (Hector) the higher the correlation and low RMSE between CMIP5 models and MAGICC6.Those points with a SD similar to that of Hector experience the same amplitude of temperature change over this time period (MAG-ICC6).All of the models are highly correlated with Hector, with a large range in the SD (1-5 • C).
Figures 10 and 11 present a detailed view of carbon fluxes under RCP 8.5, for CMIP5 and observations.The ocean is a major sink of carbon through 2100, becoming less Introduction

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Full effective with time in both Hector and the CMIP5 models.MAGICC6 does not include air-sea fluxes in its output, and because it is not open source we were unable to obtain these values.Therefore, we compare air-sea fluxes of CO 2 to MAGICC5.3, the version currently used in the IA model, Global Change Assessment Model, updated with explicit BC and OC forcing as described in Smith and Bond (2014).The correlation is high between Hector and CMIP5 over the historical period (0.95).However, the correlation drops off significantly between 2005 and 2300 (0.10) (Table 4c).This is an active area of research, investigating the differences between Hector and CMIP5 after 2100.One potential reason for the low correlation after 2100 could be due to the fact that we are only comparing to three models that run the RCP extension to 2300 (bcc-csm1-1, IPSL-CM5A-LR, and MPI-ESM-LR).With a larger spread of fluxes, Hector may be better correlated.The average correlation over the CMIP5 models over 1850-2300 is higher at 0.80, with a RMSE of 1.45 Pg C yr −1 (Table 4b).The land fluxes have a large range of uncertainty into the future within the CMIP5 models.Hector follows the general trends of the land acting as a sink of carbon initially with a gradual switch to a carbon source after 2150.Fluxes of carbon over the land are less well correlated to the CMIP5 median compared to the air-sea fluxes, 0.55 (historical) and 0.65 (RCP 8.5).Both land and ocean fluxes within Hector agree well the observations from LeQuere et al., (2013).Lastly, a unique feature of Hector is its ability to actively solve the carbonate system in the upper ocean.This feature allows us to predict ocean acidification, calcium carbonate saturations and other parameters of the carbonate system.Figure 12 shows low latitude (< 55) pH for Hector compared to CMIP5 and observations from 1850 to 2100 under RCP 8.5.We see a significant drop in pH from present day through 2100.

Conclusions
Hector reproduces the large scale couplings and feedbacks on the climate system between the atmosphere, ocean, and land.Hector falls within the range of the CMIP5 model spread and tracks well with MAGICC.Our goal was not to simulate the fine

GMDD Introduction
Full details or parameterizations typically found in large scale complex models, but instead to represent only the most critical global processes.This allows for fast execution times, ease of understanding and straightforward analysis of the model output.To help with the analysis of Hector we included within the online database of Hector, R scripts to process Hector's output as well as the comparison data.
Hector's two key features are its open source license and modular design.This allows the user to manipulate the input files, enable/disable/replace components, or include components not found within the core version of Hector.For example, the user can design a new submodel (e.g., sea-ice) to answer specific climate questions relating to that process.Because of these critical features, Hector has the potential to be a key analytical tool in both the policy and scientific communities.We welcome user input and encourage use, modifications, and collaborations with Hector.
While Hector has many strengths, there are a few limitations that later versions of Hector hope to address.For example, Hector does not have differential radiative forcing and atmospheric temperature calculations over land and ocean.The land responds to changes in emissions of greenhouse gases, and aerosols much quicker than the ocean, leading to different temperature responses over the land and ocean.Also, Hector does not explicitly deal with oceanic heat uptake.Surface temperatures are calculated based on a linear relationship with atmospheric temperature and heat uptake by the ocean is parameterized by a constant heat uptake efficiency.While Hector can reproduce global trends in atmospheric CO 2 , and temperature, we cannot investigate ocean heat uptake in the deep ocean using Hector.Currently, there is placeholder in Hector for a more sophisticated sea-level rise submodel.The current edition of Hector uses inputs of concentrations of CH Laboratory's Global Change Assessment Model to begin running policy relevant experiments.Hector has the ability to be a key analytical tool used across many scientific and policy communities due to its modern software architecture, open source, and object-oriented structure.

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Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | For each biome i , NPP and RH are computed as functions of their preindustrial values NPP 0 and RH 0 , current atmospheric carbon C atm , and the Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 19-25, 27).Radiative forcing is reported as the relative radiative forcing.The base year user-specified forcings are subtracted from the total radiative forcing to yield a forcing relative to the base year.In the current model of Hector, the gases other than CO 2 are only used for the calculation of radiative forcing.Discussion Paper | Discussion Paper | Discussion Paper | are calculated by the CH 4 concentration and the emissions of three primary pollutants: NO x , CO, and NMVOCs: Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | [CO 2 ] in 1850 is 286.0 ppmv, comparing well with observations from Law Dome of 285.2 ppmv.Discussion Paper | Discussion Paper | Discussion Paper |

Figure 6
compares atmospheric [CO 2 ] from Hector and MAGICC6 under all four RCP scenarios out to 2300.Hector is well correlated with MAGICC6 from 1850 out to 2300 for the four RCPs.Under all of the scenarios except for RCP 8.5, atmospheric [CO 2 ] within Hector fluctuates around the MAGICC6 atmospheric [CO 2 ] values, with the most notable fluctuations under low carbon emissions.This is due to changes in the flux Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 4 and N 2 O to calculate radiative forcing from CH 4 and N 2 O. Ideally we would like Hector to calculate concentrations from emissions of CH 4 and N 2 O.This would allow for quick integration within IAMs.Future plans with Hector include addressing some of the above limitations and conducting numerous scientific experiments, using Hector as a stand-alone simple climate carbon-cycle model.Also, Hector will be incorporated into Pacific Northwest National Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Ratto, M., Castelletti, A., and Pagano, A.: Emulation techniques for the reduction and sensitivity analysis of complex environmental models, Environ.Modell.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 1 .
Figure 1.Model phases for the coupler (left) and a typical component (right).Arrows show flow of control and data.The greyed spinup step is optional.

Table 1 .
Initial model conditions prior to spinup, assuming a pre-industrial steady state.

Table 2 .
Model parameters for the land and ocean carbon components.