Carbon Cycle Model Intercomparison Project Research Articles

Evaluating tree-ring proxies for representing the ecosystem productivity in India.

Terrestrial ecosystems are one of the major sinks of atmospheric CO2 and play a key role in climate change mitigation. Forest ecosystems offset nearly 25% of the global annual CO2 emissions, and a large part of this is stored in the aboveground woody biomass. Several studies have focused on understanding the carbon sequestration processes in forest ecosystems and their response to climate change using the eddy covariance (EC) technique and remotely sensed vegetation indices. However, very few of them address the linkage of tree-ring growth with the ecosystem-atmosphere carbon exchange, and nearly none have tested this linkage over a long-term (> 100 years) - limited by the short-term (< 50 years) availability of measured ecosystem carbon flux. Nevertheless, tree-ring indices can potentially act as proxies for ecosystem productivity. We utilise the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) model outputs for its 140-year-long simulated records of mean monthly gross primary productivity (GPP) and compare them with the tree-ring growth indices over the northwestern Himalayan region in India. In this study, we examine three coniferous tree species: Pinus roxburghii and Picea smithiana wall. Boiss and Cedrus deodara and find that the strength of the correlation between GPP and tree ring growth indices (RWI) varies among the species.

Global and northern-high-latitude net ecosystem production in the 21st century from CMIP6 experiments

Abstract. Climate warming is accelerating the changes in the global terrestrial ecosystems and particularly those in the northern high latitudes (NHLs; poleward of 50∘ N) and rendering the land–atmosphere carbon exchange highly uncertain. The Coupled Model Intercomparison Project Phase 6 (CMIP6) employs the most updated climate models to estimate terrestrial ecosystem carbon dynamics driven by a new set of socioeconomic and climate change pathways. By analyzing the future (2015–2100) carbon fluxes estimated by 10 CMIP6 models, we quantitatively evaluated the projected magnitudes, trends, and uncertainties in the global and NHL carbon fluxes under four scenarios plus the role of NHLs in the global terrestrial ecosystem carbon dynamics. Overall, the models suggest that the global and NHL terrestrial ecosystems will be consistent carbon sinks in the future, and the magnitude of the carbon sinks is projected to be larger under scenarios with higher radiative forcing. By the end of this century, the models on average estimate the NHL net ecosystem productivity (NEP) as 0.54 ± 0.77, 1.01 ± 0.98, 0.97 ± 1.62, and 1.05 ± 1.83 Pg C yr−1 under SSP126, SSP245, SSP370, and SSP585, respectively. The uncertainties are not substantially reduced compared with earlier results, e.g., the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP). Although NHLs contribute a small fraction of the global carbon sink (∼ 13 %), the relative uncertainties in NHL NEP are much larger than the global level. Our results provide insights into future carbon flux evolutions under future scenarios and highlight the urgent need to constrain the large uncertainties associated with model projections for making better climate mitigation strategies.

Limitations of the 1 % experiment as the benchmark idealized experiment for carbon cycle intercomparison in C4MIP

Abstract. Idealized climate change simulations are used as benchmark experiments to facilitate the comparison of ensembles of climate models. In the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the 1 % per yearly compounded change in atmospheric CO2 concentration experiment was used to compare Earth system models with full representations of the global carbon cycle in the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP). However, this “1 % experiment” was never intended for such a purpose and implies a rise in atmospheric CO2 concentration at double the rate of the instrumental record. Here, we examine this choice by using an intermediate complexity climate model to compare the 1 % experiment to an idealized CO2 pathway derived from a logistic function. The comparison shows three key differences in model output when forcing the model with the logistic experiment. (1) The model forced with the logistic experiment exhibits a transition of the land biosphere from a carbon sink to a carbon source, a feature absent when forcing the model with the 1 % experiment. (2) The ocean uptake of carbon comes to dominate the carbon cycle as emissions decline, a feature that cannot be captured when forcing a model with the 1 % experiment, as emissions always increase in that experiment. (3) The permafrost carbon feedback to climate change under the 1 % experiment forcing is less than half the strength of the feedback seen under logistic experiment forcing. Using the logistic experiment also allows smooth transition to zero or negative emissions states, allowing these states to be examined without sharp discontinuities in CO2 emissions. The protocol for the CMIP6 iteration of C4MIP again sets the 1 % experiment as the benchmark experiment for model intercomparison; however, clever use of the Tier 2 experiments may alleviate some of the limitations outlined here. Given the limitations of the 1 % experiment as the benchmark experiment for carbon cycle intercomparisons, adding a logistic or similar idealized experiment to the protocol of the CMIP7 iteration of C4MIP is recommended.

Earth system model simulations show different feedback strengths of the terrestrial carbon cycle under glacial and interglacial conditions

Abstract. In simulations with the MPI Earth System Model, we study the feedback between the terrestrial carbon cycle and atmospheric CO2 concentrations under ice age and interglacial conditions. We find different sensitivities of terrestrial carbon storage to rising CO2 concentrations in the two settings. This result is obtained by comparing the transient response of the terrestrial carbon cycle to a fast and strong atmospheric CO2 concentration increase (roughly 900 ppm) in Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP)-type simulations starting from climates representing the Last Glacial Maximum (LGM) and pre-industrial times (PI). In this set-up we disentangle terrestrial contributions to the feedback from the carbon-concentration effect, acting biogeochemically via enhanced photosynthetic productivity when CO2 concentrations increase, and the carbon–climate effect, which affects the carbon cycle via greenhouse warming. We find that the carbon-concentration effect is larger under LGM than PI conditions because photosynthetic productivity is more sensitive when starting from the lower, glacial CO2 concentration and CO2 fertilization saturates later. This leads to a larger productivity increase in the LGM experiment. Concerning the carbon–climate effect, it is the PI experiment in which land carbon responds more sensitively to the warming under rising CO2 because at the already initially higher temperatures, tropical plant productivity deteriorates more strongly and extratropical carbon is respired more effectively. Consequently, land carbon losses increase faster in the PI than in the LGM case. Separating the carbon–climate and carbon-concentration effects, we find that they are almost additive for our model set-up; i.e. their synergy is small in the global sum of carbon changes. Together, the two effects result in an overall strength of the terrestrial carbon cycle feedback that is almost twice as large in the LGM experiment as in the PI experiment. For PI, ocean and land contributions to the total feedback are of similar size, while in the LGM case the terrestrial feedback is dominant.

Biological production in the Indian Ocean upwelling zones –Part 1: refined estimation via the use of a variable compensation depth in ocean carbon models

Abstract. Biological modelling approach adopted by the Ocean Carbon-Cycle Model Intercomparison Project (OCMIP-II) provided amazingly simple but surprisingly accurate rendition of the annual mean carbon cycle for the global ocean. Nonetheless, OCMIP models are known to have seasonal biases which are typically attributed to their bulk parameterisation of compensation depth. Utilising the criteria of surface Chl a-based attenuation of solar radiation and the minimum solar radiation required for production, we have proposed a new parameterisation for a spatially and temporally varying compensation depth which captures the seasonality in the production zone reasonably well. This new parameterisation is shown to improve the seasonality of CO2 fluxes, surface ocean pCO2, biological export and new production in the major upwelling zones of the Indian Ocean. The seasonally varying compensation depth enriches the nutrient concentration in the upper ocean yielding more faithful biological exports which in turn leads to accurate seasonality in the carbon cycle. The export production strengthens by ∼ 70 % over the western Arabian Sea during the monsoon period and achieves a good balance between export and new production in the model. This underscores the importance of having a seasonal balance in the model export and new productions for a better representation of the seasonality of the carbon cycle over upwelling regions. The study also implies that both the biological and solubility pumps play an important role in the Indian Ocean upwelling zones.

Biogeochemical protocols and diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP)

Abstract. The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948–2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.

Fluks CO2 di Perairan Pesisir Pulau Lombok, Nusa Tenggara Barat

&lt;strong&gt;CO&lt;sub&gt;2&lt;/sub&gt; Flux in the Coastal Waters of Lombok, West Nusa Tenggara.&lt;/strong&gt; Coastal and marine areas play an important role in climate regulation by absorbing CO&lt;sub&gt;2&lt;/sub&gt; and exchanging carbon with various carbon storing compartments as the atmosphere, land and biota. This study aims to determine the exchange of CO2 between the sea surface to the atmosphere in the coastal waters of the island of Lombok and its components sink and source, and to know the parameters that influence the process. Field observation was carried out during April, 2012 in Sekotong Bay (10 Stations) and Kodek Bay (6 Stations).The partial pressure of CO&lt;sub&gt;2&lt;/sub&gt; (pCO&lt;sub&gt;2&lt;/sub&gt;) in sea surface level, was calculated using ABIOTIC model of the ocean carbon cycle model intercomparison project phase-2 (OCMIP-2). The results showed that generally Kodek Bay waters and outside of Sekotong Bay act as a sink of CO2 from the atmosphere, while inside of Sekotong Bay act as source of CO&lt;sub&gt;2&lt;/sub&gt; to the atmosphere.CO&lt;sub&gt;2&lt;/sub&gt; flux in Sekotong bay waters and Kodek bay waters each ranging between -0.61 to 0.52 and -0.80 to -1.84 mmol.m&lt;sup&gt;-2&lt;/sup&gt;.d&lt;sup&gt;-1&lt;/sup&gt; with an average of 0.05 ± 0.40 and -1.29 ± 0.40 mmol.m&lt;sup&gt;-2&lt;/sup&gt;.d&lt;sup&gt;-1&lt;/sup&gt;. Kodek Bay act as CO&lt;sub&gt;2&lt;/sub&gt; sink because influence by water mass from Lombok Strait that have high concentrations of nutrient and chlorophyll. The existence of seagrass and coral reefs that support small islands in the Kodek bay helped reduce elevated pCO&lt;sub&gt;2&lt;/sub&gt; water column and strengthen the absorption of CO&lt;sub&gt;2&lt;/sub&gt; in these waters. Sekotong bay inside, acts as a release of CO&lt;sub&gt;2&lt;/sub&gt;, because the waters of the Sekotong bay tend to be closed, so that the residence time of water masses becomes longer resulting in enrichment of DIC and pCO&lt;sub&gt;2&lt;/sub&gt;.

C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

Abstract. Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate–carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate–carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1 % per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.

Improved routines to model the ocean carbonate system: mocsy 2.0

Abstract. Modelers compute ocean carbonate chemistry often based on code from the Ocean Carbon Cycle Model Intercomparison Project (OCMIP), last revised in 2005. Here we offer improved publicly available Fortran 95 routines to model the ocean carbonate system (mocsy 2.0). Both codes take as input dissolved inorganic carbon CT and total alkalinity AT, tracers that are conservative with respect to mixing and changes in temperature and salinity. Both use the same thermodynamic equilibria to compute surface-ocean pCO2 and simulate air–sea CO2 fluxes, but mocsy 2.0 uses a faster and safer algorithm (SolveSAPHE) to solve the alkalinity-pH equation, applicable even under extreme conditions. The OCMIP code computes only surface pCO2, while mocsy computes all other carbonate system variables throughout the water column. It also avoids three common model approximations: that density is constant, that modeled potential temperature is equal to in situ temperature, and that depth is equal to pressure. Errors from these approximations grow with depth, e.g., reaching 3% or more for pCO2, H+, and ΩA at 5000 m. The mocsy package uses the equilibrium constants recommended for best practices. It also offers two new options: (1) a recently reassessed total boron concentration BT that is 4% larger and (2) new K1 and K2 formulations designed to include low-salinity waters. Although these options enhance surface pCO2 by up to 7 μatm, individually, they should be avoided until (1) best-practice equations for K1 and K2 are reevaluated with the new BT and (2) formulations of K1 and K2 for low salinities are adjusted to be consistent among pH scales. The common modeling practice of neglecting alkalinity contributions from inorganic P and Si leads to substantial biases that could easily be avoided. With standard options for best practices, mocsy agrees with results from the CO2SYS package within 0.005% for the three inorganic carbon species (concentrations differ by less than 0.01 μmol kg−1). Yet by default, mocsy's deep-water fCO2 and pCO2 are many times larger than those from older packages, because they include pressure corrections for K0 and the fugacity coefficient.

Air-Sea Co2 Gas Exchange In Nasik Strait Waters, Belitung

Marine carbonate system plays an important role in the air-sea CO2 gas exchange. The aim of the present study is to investigate the air-sea flux of CO2 in Nasik Strait, Belitung. Field observation was carried out during April, 2010, where the observed parameters are consisted of temperature, salinity, pH, dissolved inorganic carbon (DIC), total alkalinity (TA), primary productivity of phytoplankton and nutrients (phosphate and silicate). Partial pressure of CO2 (pCO2) in sea surface was particularly calculated using ABIOTIC model of the ocean carbon cycle model intercomparison project phase-2. Analysis results of the marine carbonate system show that generally Nasik Strait waters is played as a source (release) of CO2 to the atmosphere. The CO2 flux in mangrove waters, coral reef waters and coastal waters (non mangrove and coral reef) ranged between 3.06–3.19, 0.96–1.45 and 2.77–2.98 mmolC/m2/day, respectively. The present study found that the CO2 uptake by phytoplankton (photosynthesis) was not significantly affect the CO flux, however the decomposition of particulate organic carbon tended to give significant contribution to the CO2 flux.

Quantifying Carbon Cycle Feedbacks

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

Challenges to Reproduce Vegetation Structure and Dynamics in Amazonia Using a Coupled Climate–Biosphere Model

Abstract The Amazon rain forest constitutes one of the major global stocks of carbon. Recent studies, including the last Intergovernmental Panel on Climate Change report and the Coupled Climate Carbon Cycle Model Intercomparison Project, have suggested that it may reduce in size and lose biomass during the twenty-first century through a savannization process. A better understanding of how this ecosystem structure, dynamics, and carbon balance may respond to future climate changes is needed. This article investigates how well a fully coupled atmosphere–biosphere model can reproduce vegetation structure and dynamics in Amazonia to the extent permitted by available data. The accurate representation of the coupled climate–biosphere dynamics requires the accurate representation of climate, net primary production (NPP), and its partition among the several carbon pool components. The simulated climate is validated against precipitation (within 5% of four datasets) and incident solar radiation (within 7% of observations). The authors also validate (i) simulated land cover, which reproduces well the observed patterns; (ii) NPP, within 5% of observations; and (iii) respiration rates, within 15% of observations. The performance of simulated variables that depend on carbon allocation, like NPP partitioning, leaf area index, and aboveground live biomass, although good on a regional mean, is significantly low when spatial patterns are considered. These errors may be attributed to fixed carbon allocation and residence time parameters assumed by the model. Carbon allocation apparently varies spatially, and to simulate this spatial variability is quite a challenge.

Characterizing uncertainty in modeling primary terrestrial ecosystem processes

The simulation results from models participating in the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) highlight the role of positive carbon‐climate feedback in accelerating growth of atmospheric CO2. The large range among models in the strength of this feedback indicates the uncertainty in our understanding of the response of the land and the oceans to continued climate warming and increasing CO2. Most of this uncertainty is associated with the response of terrestrial ecosystems to changes in climate and atmospheric CO2. The reasons for differences between models' responses to transient climate and CO2 forcing are not easily identified because of their complex parameterizations and spatially distributed processes. In this paper, we show that a simple box model can reasonably reproduce the globally averaged primary land‐atmosphere CO2 fluxes and carbon pools of two complex terrestrial ecosystem models (TEMs) over a range of emission scenarios. The parameters of the box model are calculated by fitting the box model to each TEM's response to transient climate and CO2 forcing. This approach is also applied to terrestrial carbon cycle components of carbon‐climate models participating in the C4MIP study. The resulting set of parameter values based on a common box model structure yields a wide range of parameter values, which suggests an absence of clear consensus in modeling primary terrestrial ecosystem processes and provides some insight into the reasons for divergent responses of terrestrial carbon cycle components.

Design and Validation of an Offline Oceanic Tracer Transport Model for a Carbon Cycle Study

Abstract An offline passive tracer transport model with self-operating diagnostic-mode vertical mixing and horizontal diffusion parameterizations is used with assimilated ocean currents to find the chlorofluorocarbon (CFC-11) cycle in oceans. This model was developed at the National Institute for Environmental Studies (NIES) under the carbon cycle research project inside the Greenhouse Gas Observing Satellite (GOSAT) modeling group. The model borrows offline fields from precalculated monthly archives of assimilated ocean currents, temperature, and salinity, and it evolves a prognostic passive tracer with prescribed surface forcing. The model’s performance is validated by simulating the CFC-11 cycle in the ocean starting from the preindustrial period (1938) with observed anthropogenic perturbations of atmospheric CFC-11 to comply with the Ocean Carbon-Cycle Model Intercomparison Project Phase-2 (OCMIP-2) flux protocol. The model results are compared with ship observations as well as the results of candidate models of OCMIP-2 and a performance is assessed. The model simulates the deep-ventilation processes in the Atlantic Ocean appreciably well and yields a good agreement in the column inventory of the CFC-11 amplitude and phase compared to the observation. The statistical skill test shows that this model outperforms other candidate models of OCMIP-2 because of its higher resolution and assimilated offline input feeding, and it shows a potential role in improving transport calculation in the ocean with cost-effective computation.

Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century?

Global warming caused by anthropogenic CO2 emissions is expected to reduce the capability of the ocean and the land biosphere to take up carbon. This will enlarge the fraction of the CO2 emissions remaining in the atmosphere, which in turn will reinforce future climate change. Recent model studies agree in the existence of such a positive climate–carbon cycle feedback, but the estimates of its amplitude differ by an order of magnitude, which considerably increases the uncertainty in future climate projections. Therefore we discuss, in how far a particular process or component of the carbon cycle can be identified, that potentially contributes most to the positive feedback. The discussion is based on simulations with a carbon cycle model, which is embedded in the atmosphere/ocean general circulation model ECHAM5/MPI-OM. Two simulations covering the period 1860–2100 are conducted to determine the impact of global warming on the carbon cycle. Forced by historical and future carbon dioxide emissions (following the scenario A2 of the Intergovernmental Panel on Climate Change), they reveal a noticeable positive climate–carbon cycle feedback, which is mainly driven by the tropical land biosphere. The oceans contribute much less to the positive feedback and the temperate/boreal terrestrial biosphere induces a minor negative feedback. The contrasting behavior of the tropical and temperate/boreal land biosphere is mostly attributed to opposite trends in their net primary productivity (NPP) under global warming conditions. As these findings depend on the model employed they are compared with results derived from other climate–carbon cycle models, which participated in the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP).

Water Mass Distribution and Ventilation Time Scales in a Cost-Efficient, Three-Dimensional Ocean Model

Abstract A cost-efficient, seasonally forced three-dimensional frictional geostrophic balance ocean model (Bern3D) has been developed that features isopycnal diffusion and Gent–McWilliams transport parameterization, 32 depth layers, and an implicit numerical scheme for the vertical diffusion. It has been tuned toward observed chlorofluorocarbon (CFC-11) inventories and deep ocean radiocarbon signatures to reproduce the ventilation time scales of the thermocline and the deep ocean. Model results are consistent with the observed large-scale distributions of temperature, salinity, natural and bomb-produced radiocarbon, CFC-11, anthropogenic carbon, 39Ar/Ar, and estimates of the meridional heat transport. Root-mean-square errors for the temperature and salinity fields are 1 K and 0.2 psu, comparable to results from the Ocean Carbon-Cycle Model Intercomparison Project. Global inventories of CFC-11 and anthropogenic carbon agree closely with observation-based estimates. Model weaknesses include a too-weak formation and propagation of Antarctic Intermediate Water and of North Atlantic Deep Water. The model has been applied to quantify the recent carbon balance, surface-to-deep transport mechanisms, and the importance of vertical resolution for deep equatorial upwelling. Advection is a dominant surface-to-deep transport mechanism, whereas explicit diapycnal mixing is of little importance for passive tracers and contributes less than 3% to the modeled CFC-11 inventory in the Indo-Pacific. Decreasing the vertical resolution from 32 to 8 layers causes deep equatorial upwelling to increase by more than a factor of 4. Modeled ocean uptake of anthropogenic carbon is 19.7 GtonC over the decade from 1993 to 2003, comparable to an estimate from atmospheric oxygen data of 22.4 ± 6.1 GtonC.

On ocean carbon-cycle model comparison

The Ocean Carbon-Cycle Model Intercomparison Project (OCMIP) began in 1995 and is now in its 2nd, 3-year phase. What has motivated this effort?