Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models

The strength of feedbacks between a changing climate and future CO₂ concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations—in which atmospheric CO₂ levels were computed prognostically—for historical (1850-2005) and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006–2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO₂ over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO₂. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO₂ biases and future CO₂ levels for the multimodel ensemble. We used this relationship to create a contemporary CO₂ tuned model (CCTM) estimate of the atmospheric CO₂ trajectory for the 21st century. The CCTM yielded CO₂ estimates of 600±14 ppm at 2060 and 947±35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO₂, CO₂-induced radiative forcing, and CO₂-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO₂ during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO₂ from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.