Carbon cycle feedbacks are usually categorized into carbon–concentration and carbon–climate feedbacks, which arise owing to increasing atmospheric CO2 concentration and changing physical climate. Both feedbacks are often assumed to operate independently: that is, the total feedback can be expressed as the sum of two independent carbon fluxes that are functions of atmospheric CO2 and climate change, respectively. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), radiatively and biogeochemically coupled simulations have been undertaken to better understand carbon cycle feedback processes. Results show that the sum of total ocean carbon uptake in the radiatively and biogeochemically coupled experiments is consistently larger by 19–58 petagrams of carbon (Pg C) than the uptake found in the fully coupled model runs. This nonlinearity is small compared to the total ocean carbon uptake (533–676 Pg C), but it is of the same order as the carbon–climate feedback. The weakening of ocean circulation and mixing with climate change makes the largest contribution to the nonlinear carbon cycle response since carbon transport to depth is suppressed in the fully relative to the biogeochemically coupled simulations, while the radiatively coupled experiment mainly measures the loss of near-surface carbon owing to warming of the ocean. Sea ice retreat and seawater carbon chemistry contribute less to the simulated nonlinearity. The authors’ results indicate that estimates of the ocean carbon–climate feedback derived from “warming only” (radiatively coupled) simulations may underestimate the reduction of ocean carbon uptake in a warm climate high CO2 world.

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The intrinsic seasonal predictability of Arctic sea ice is investigated in a 400-yr-long preindustrial simulation performed with the Centre National de Recherches Météorologiques Coupled Global Climate Model, version 3.3 (CNRM-CM3.3). The skill of several predictors of the pan-Arctic sea ice area was quantified: the sea ice area itself, the pan-Arctic sea ice volume, and some areal predictors built from the subgrid ice thickness distribution (ITD). Sea ice area provides a potential predictability of about 3 months, which is consistent with previous studies using model and observation data. Sea ice volume predictive skill for winter sea ice area prediction is weak. Nevertheless, there is a higher potential to predict the September ice area with the June volume anomaly than with the June area anomaly. Using ITD-based predictors, two ''regimes'' of predictability were highlighted. The first one, a ''persistence regime,'' applies to winter/early spring sea ice seasonal predictability. The winter sea ice cover can be predicted in late fall/early winter from the amount of young ice formed since the freeze-up onset in the margins. However, sea ice area itself is potentially the best predictor of winter sea ice area at seasonal time scales. The second regime is a ''memory regime.'' It applies to the predictability of summer sea ice area. An ice area anomaly in September is potentially predictable up to 6 months in advance, using the area covered by ice thicker than a critical thickness lying between 0.9 and 1.5 m. Results of this study are preliminary; however, they provide information for the design of future prediction systems and highlight the need for observations and a state-of-the-art sea ice model.