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[1] What is the uncertainty of climate–carbon cycle projections in response to anthropogenic greenhouse gas emissions, and how can we reduce this uncertainty? We address this question by quantifying the ability of available ocean tracer observations to constrain the values of diapycnal diffusivity in the pelagic ocean (Kv), a key uncertain parameter representing sub-grid-scale diapycnal (vertical) mixing in physical circulation models. We show that model versions with weak mixing (i.e., low Kv) lead to higher projections of atmospheric CO2 and larger global warming than do models with vigorous mixing. Slower heat uptake and slower carbon uptake by the oceans contribute about equally to the accelerated warming in the low-mixing models. A Bayesian data-model fusion method is developed to quantify the likelihood of different structural and parametric model choices given an array of observed 20th century ocean tracer distributions. These spatially resolved observations provide strong limits on the upper value of Kv, whereas global metrics used in previous studies, such as the historical evolution of global average surface air temperature, global ocean heat uptake, or atmospheric CO2 concentration, provide only poor constraints. We compare different methods to quantify the probability of a particular diffusivity value given the observational constraints. One-dimensional, globally horizontally averaged data result in sharper probability density functions compared with the full 3-D fields. This perhaps unexpected result opens up an avenue to objectively determine the optimal degree of aggregation at which model predictions have skill, and at which observations are most helpful in constraining model parameters. Our best estimate for Kv in the pelagic pycnocline is around 0.05–0.2 cm2/s, in agreement with earlier independent estimates based on tracer dispersion experiments and turbulence microstructure measurements.

A new model of global climate, ocean circulation, ecosystems, and biogeochemical cycling, including a fully coupled carbon cycle, is presented and evaluated. The model is consistent with multiple observational data sets from the past 50 years as well as with the observed warming of global surface air and sea temperatures during the last 150 years. It is applied to a simulation of the coming two millennia following a business-as-usual scenario of anthropogenic CO2 emissions (SRES A2 until year 2100 and subsequent linear decrease to zero until year 2300, corresponding to a total release of 5100 GtC). Atmospheric CO2 increases to a peak of more than 2000 ppmv near year 2300 (that is an airborne fraction of 72% of the emissions) followed by a gradual decline to ∼1700 ppmv at year 4000 (airborne fraction of 56%). Forty-four percent of the additional atmospheric CO2 at year 4000 is due to positive carbon cycle–climate feedbacks. Global surface air warms by ∼10°C, sea ice melts back to 10% of its current area, and the circulation of the abyssal ocean collapses. Subsurface oxygen concentrations decrease, tripling the volume of suboxic water and quadrupling the global water column denitrification. We estimate 60 ppb increase in atmospheric N2O concentrations owing to doubling of its oceanic production, leading to a weak positive feedback and contributing about 0.24°C warming at year 4000. Global ocean primary production almost doubles by year 4000. Planktonic biomass increases at high latitudes and in the subtropics whereas it decreases at midlatitudes and in the tropics. In our model, which does not account for possible direct impacts of acidification on ocean biology, production of calcium carbonate in the surface ocean doubles, further increasing surface ocean and atmospheric pCO2. This represents a new positive feedback mechanism and leads to a strengthening of the positive interaction between climate change and the carbon cycle on a multicentennial to millennial timescale. Changes in ocean biology become important for the ocean carbon uptake after year 2600, and at year 4000 they account for 320 ppmv or 22% of the atmospheric CO2 increase since the preindustrial era.

[1] Positive feedbacks between climate change and the carbon cycle have the potential to amplify the growth of atmospheric carbon dioxide and accelerate future climate warming. However, both the magnitude of and the processes which drive future carbon cycle-climate feedbacks remain highly uncertain. In this study, we use a coupled climate-carbon model to investigate how the response of vegetation photosynthesis to climate change contributes to the overall strength of carbon cycle-climate feedbacks. We find that the feedback strength is particularly sensitive to the model representation of the photosynthesis-temperature response, with lesser sensitivity to the parameterization of soil moisture and nitrogen availability. In all simulations, large feedbacks are associated with a climatic suppression of terrestrial primary productivity and consequent reduction of terrestrial carbon uptake. This process is particularly evident in the tropics and can explain a large part of the range of carbon cycle-climate feedbacks simulated by different coupled climate-carbon models.